Modern statistical analysis — desktop & web, AI-assisted, reproducible.
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Statios unifies the analytical scope of SPSS, Stata and EViews in one modern application, with a built-in AI assistant and one-click, citation-ready reporting. Every figure is computed by an open, auditable Python engine that runs both on the desktop and entirely in the browser.
| Domain | Procedures | Scope |
|---|---|---|
| Descriptive Statistics & Hypothesis Tests | 37 | Descriptive statistics and statistical hypothesis tests covering parametric (t-tests, ANOVA), non-parametric (Mann-Whitney, Kruskal-Wallis, Friedman), correlation, and categorical association tests. |
| Regression & Generalized Linear Models | 142 | Statios's model namespace covers 50+ regression and generalized linear estimation procedures, from OLS to latent-variable extensions. Methods range from standard parametric models (linear, logistic, count) to specialized estimators for endogenous/selected samples, censoring, and complex errors. All predictor-taking handlers auto-expand categorical predictors R-style (labeled indicator columns, named reference level, factors field) — no manual dummy coding required. |
| Post-Estimation & Diagnostics | 47 | Statios provides 31 post-estimation and regression diagnostic procedures spanning heteroskedasticity tests, serial correlation diagnostics, influence measures, marginal effects, contrasts, and model comparison tools. |
| Time Series & Macroeconometrics | 84 | Comprehensive time series analysis, DSGE models, and advanced forecasting. Includes unit root tests, ARIMA/seasonal models, VAR/cointegration, impulse responses, nonlinear models, forecast evaluation, and mixed-frequency regression. |
| Panel Data & Causal Inference | 51 | Statios supports canonical panel data estimators (FE, RE, Hausman test, 2SLS-IV), advanced dynamic panel methods (Arellano-Bond, System GMM), and modern causal inference designs (DiD, RDD, synthetic control, staggered DiD, IPW, mediation analysis). |
| Multivariate, Factor & Latent-Variable Analysis | 35 | Covers exploratory and confirmatory dimensionality reduction, clustering, reliability, latent structure discovery, and item response modeling — from classical PCA/factor analysis through modern IRT, SEM, and categorical methods. |
| Survival, Bayesian, Meta-Analysis & Resampling | 68 | Statistical methods for survival analysis, Bayesian inference, meta-analytic pooling, and resampling-based inference. Includes Kaplan-Meier curves, Cox proportional-hazards regression, parametric and competing-risks models, Bayesian linear/logistic/hierarchical regression with MCMC diagnostics, fixed- and random-effects meta-analysis, and bootstrap/jackknife/permutation methods. |
| Machine Learning & Choice Models | 31 | Statios provides machine learning and choice modeling tools: supervised learning with explainability (Random Forest, Gradient Boosting, SVM, Decision Trees, neural networks); model evaluation (cross-validation, ROC/AUC, confusion matrix, feature importance, SHAP); and conjoint analysis (traditional, choice-based, hierarchical Bayes) for preference modeling. |
| Complex Survey & Missing Data | 18 | Statios provides complex survey design analysis and missing data handling. Covers design-based inference (Taylor linearization, replicate weights, Rao-Scott corrections) and advanced imputation (MICE, EM, PMM) with proper uncertainty propagation. |
| Data Management, Transforms & Workflow | 68 | Data management, transforms and workflow procedures in Statios: dataset loading and multi-dataset workflows, direct cell editing with undo/redo, spreadsheet-style formulas for derived variables, variable metadata configuration (types, labels, measurement levels), data transformations (compute, recode, categorize, standardize, filter, lag/lead, dummy), data manipulation (merge, reshape, aggregate, collapse, append, sort, subset, deduplicate, rank, transpose, split/concat columns, pivot), data-to-table formatting, and session/macro recording for reproducible workflows. |
| AI, Automation, Graphics & Reporting | 19 | AI-powered statistical guidance, automated analysis pipelines, publication-quality graphics and report generation, plus a visual rule-based workflow recommender (Guided Path) and a node-based pipeline editor (RLab). |
These work today, with a caveat worth knowing.
| Capability | Area | Limitation |
|---|---|---|
| Regression Discontinuity (Sharp RDD) | Regression & Generalized Linear Models | Bandwidth selection is ad-hoc (1.5*SD rule), not data-driven MSE-optimal. |
| Extended Regression Model — Probit (ERM-Probit) | Regression & Generalized Linear Models | IMR lambda SE not corrected for generated-regressor problem (Murphy-Topel correction not implemented). |
| Extended Regression Model — Ordered (ERM-Ordered) | Regression & Generalized Linear Models | Lambda SE uncorrected; no p-values reported due to generated-regressor issue. |
| Treatment effect contrasts (ATE/ATT/ATC) | Post-Estimation & Diagnostics | Uses OLS-based potential outcomes, not designed for instrumental variables. |
| Diffusion-index direct forecast (user-supplied factors) | Time Series & Macroeconometrics | NOT a true Stock-Watson Dynamic Factor Model: there is no latent-factor extraction (no PCA / Kalman / state-space estimation of common factors inside the method) — the factors must be supplied as columns. Automatic latent-factor extraction is a separate upcoming feature. |
| Unrestricted MIDAS regression | Time Series & Macroeconometrics | Currently uses same dataset for both frequencies (pending multi-dataset resolution). |
| Almon polynomial MIDAS | Time Series & Macroeconometrics | Uses single dataset for both frequencies (pending multi-dataset support). |
| Beta polynomial MIDAS | Time Series & Macroeconometrics | Single-dataset limitation; multi-dataset pending (Phase 18). |
| Exponential Almon MIDAS | Time Series & Macroeconometrics | Single dataset; multi-dataset architecture pending. |
| MIDAS nowcasting | Time Series & Macroeconometrics | Single-dataset implementation; true multi-frequency data awaits Phase 18. |
| MIDAS model comparison | Time Series & Macroeconometrics | Single-dataset; intended for separate high/low datasets pending multi-dataset manager. |
| Real Business Cycle model | Time Series & Macroeconometrics | Log-linearized first-order approximation only; no occasionally binding constraints. |
| New Keynesian 3-equation model | Time Series & Macroeconometrics | Simplified linearized form; no ELB, expectations-driven dynamics approximate. |
| DSGE calibration | Time Series & Macroeconometrics | Moment-matching heuristic only; not Bayesian estimation or SMM. |
| Regression Discontinuity (Sharp RDD) | Panel Data & Causal Inference | Bandwidth selection is ad-hoc (not Imbens-Kalyanaraman or Calonico-Cattaneo-Titiunik data-driven optimal); local-linear only. |
| Propensity Score Matching (PSM) | Panel Data & Causal Inference | Uses naive paired SE; does not apply Abadie-Imbens (2006) variance correction for estimated scores and matching with replacement. |
| Hierarchical Agglomerative Clustering | Multivariate, Factor & Latent-Variable Analysis | O(n²) memory constraint; refuses n > 20,000 rows. |
| IRT Fit Indices (Approximate) | Multivariate, Factor & Latent-Variable Analysis | Approximate method: fixed-1PL reference and diagonal weight matrix, not full Maydeu-Olivares M2. |
| Shared Frailty Cox Model | Survival, Bayesian, Meta-Analysis & Resampling | Gamma frailty only; variance estimated via martingale residuals. |
| Bayesian Hierarchical Model | Survival, Bayesian, Meta-Analysis & Resampling | Fallback uses OLS-shrinkage approximation without full MCMC; ICC computed from variance components. |
| Bayes Factor | Survival, Bayesian, Meta-Analysis & Resampling | BIC-based approximation; not exact marginal likelihood. |
| SHAP Values | Machine Learning & Choice Models | TreeExplainer unavailable in browser (WASM); fallback to KernelExplainer (sampled Shapley values, not exact TreeSHAP). |
| CHAID | Machine Learning & Choice Models | Stub implementation; returns placeholder. |
| QUEST | Machine Learning & Choice Models | Stub implementation; returns placeholder. |
| C5.0 | Machine Learning & Choice Models | Stub implementation; returns placeholder. |
| Ensemble Trees Comparison | Machine Learning & Choice Models | Stub implementation; returns placeholder. |
| Tree Export | Machine Learning & Choice Models | Stub implementation; returns placeholder. |
| Compare Trees | Machine Learning & Choice Models | Stub implementation; returns placeholder. |
| Hierarchical Bayes CBC | Machine Learning & Choice Models | Empirical-Bayes / EM-style scheme, NOT Gibbs/Metropolis; samples are posterior modes, not draws; heterogeneity SD understates posterior uncertainty. |
| Session Undo | Data Management, Transforms & Workflow | Session-level undo not fully wired; edit.undo handles cell-level reversals. |
| Session Redo | Data Management, Transforms & Workflow | Session-level redo not fully wired; edit.redo handles cell-level reversals. |
| Large-dataset/columnar mode: bounded analysis suite | data | WASM in-browser path capped at 1.5M rows or 900MB. CSV files up to 50M rows route to columnar store (Parquet/NPY), but only subset of analyses support column-on-demand (streaming). Binary formats (.xlsx/.sav/.dta) cannot be streamed. |
| WASM/Pyodide compatibility: numba and SHAP slower without JIT | ml | Web build stubs numba (Pyodide lacks LLVM JIT) and SHAP's _kernel_lib. Code runs interpreted (correct numerically, ~2–5× slower than compiled). KernelExplainer works; TreeExplainer unavailable in WASM. |
| Charts: 4 types shipped, 31 roadmap items incomplete | graphs | Implemented: histogram, scatter, boxplot, bar. Missing: violin, density, Q–Q, interaction, forest, ROC, funnel, survival curves, residual diagnostic panels, and 21 others from Phase 15 roadmap. |
| Extended regression diagnostics: some estimators in pre-release rigor improvements | regression | Arellano-Bond, GMM, DSGE solvers present but some advanced econometric estimators have rigor enhancements on unmerged branches. Shipped versions work; see code docstrings for limitations (e.g., 'simplified solver', 'bounded lag range'). |
| Item | Area | Notes |
|---|---|---|
| Statios web pivot: Python engine to Pyodide/WASM | infrastructure | W1–W4 phases (June 2026): migrate sidecar to in-browser Pyodide via WorkerTransport. Desktop Tauri shell frozen. PWA deployment, OPFS file handling, web deploy pipeline. Completed handler count remains 445; no new features, transport refactor only. |
| Charts Expansion: 31 missing chart types | graphs | Violin, density (KDE), ridgeline, Q–Q, P–P, interaction plot, mosaic, ACF/PACF, IRF with CI, residual diagnostics (4-panel), forest/funnel, survival, ROC, Bland-Altman, coefficient, marginal effects, control chart, caterpillar. Phase 15 completion. |
| AI Orchestrator & Multi-Model Routing | ai | Post-W4: intelligent provider selection, prompt caching, fallback logic, cost tracking. Statios relay authentication for managed web tier. |
| Quality backlog: UI refinements, performance, test coverage | polish | Post-W4: Monaco editor for scripts, keyboard shortcuts, undo/redo on data transforms, canvas DataGrid for >10k rows, performance audits on large-dataset analyses. |
| Item | Area | Notes |
|---|---|---|
| Advanced causal inference: bounds & sensitivity analyses | causal | Lee bounds, Rosenbaum sensitivity, E-values. Handlers present (causal.lee_bounds, etc.) but backlog for UI polish and help content. |
| Exotic time-series models: TAR/STAR, NARDL, nowcasting | timeseries | Handlers exist (ts.tar, ts.nardl, ts.nowcasting) but limited test coverage and optional dependencies (ruptures for Bai–Perron). Deferred to post-quality phase. |
| Multivariate: MANOVA, discriminant, canonical correlation | analysis | Handlers registered (test.manova, analysis.discriminant, analysis.canonical_correlation) but frontend UI tabs not yet exposed in AnalysisModal. Feature-complete backend; UI routing needed. |
| Bayesian posterior predictive checks & Bayes factors | bayes | Handlers: bayes.ppc, bayes.credible_interval, bayes.prior_posterior, bayes.bayes_factor. Deps (PyMC, ArviZ) optional. Lower priority; posterior work focuses on MCMC diagnostics. |
| Optimal scaling & ERM: categorical regression (CATREG), optimal transforms | analysis | Handlers: analysis.catreg, analysis.catpca, analysis.overals, analysis.catcorr. Phase 23 backends exist but semi-automated UI guidance and diagnostic plots deferred. |
Descriptive statistics and statistical hypothesis tests covering parametric (t-tests, ANOVA), non-parametric (Mann-Whitney, Kruskal-Wallis, Friedman), correlation, and categorical association tests.
| Procedure | What it does | Status |
|---|---|---|
| Descriptive statistics | Mean, median, variance, skewness, kurtosis, quantiles for numeric variables. | Full |
| Frequency distribution | Frequency tables with counts, percentages, cumulative percentages for categorical/numeric variables. | Full |
| One-sample t-test | Test if sample mean differs from hypothesized value; reports t-statistic, 95% CI, Cohen's d. | Full |
| Independent samples t-test | Compare means of two independent groups; includes Levene's test, optional Welch correction. | Full |
| Paired t-test | Compare means of two related/paired samples; reports difference mean, SE, 95% CI, effect size. | Full |
| One-way ANOVA | Compare means across 3+ groups; includes Levene's test, Tukey HSD post-hoc, eta/omega-squared. | Full |
| Welch's ANOVA | Heteroscedastic one-way ANOVA without equal-variance assumption; Games-Howell post-hoc. | Full |
| Two-way ANOVA | Test main effects and interaction of two factors; includes partial eta-squared effect sizes. | Full |
| Repeated measures ANOVA | Within-subjects ANOVA for 2+ repeated measures; Mauchly sphericity, GG/HF corrections. | Full |
| Yuen's trimmed t-test | Robust two-group t-test using 20% trimmed means; insensitive to heavy tails and outliers. | Full |
| Mixed ANOVA | Split-plot design: between-subject factor and within-subject repeated measures. | Full |
| Dunnett's test | Post-hoc test comparing each treatment group to a control group. | Full |
| Cochran-Armitage test | Trend test for proportions across ordered groups. | Full |
| Mann-Whitney U test | Non-parametric two-group test; exact/asymptotic p-values, rank-biserial effect size. | Full |
| Wilcoxon signed-rank test | Non-parametric paired test; exact/asymptotic p-values, effect size r. | Full |
| Kruskal-Wallis H test | Non-parametric 3+ group test; Dunn post-hoc with Bonferroni correction. | Full |
| Friedman test | Non-parametric repeated measures test; Kendall's W effect size. | Full |
| Chi-square test of independence | Association between two categorical variables; includes Cramér's V, Yates correction disclosure. | Full |
| Chi-square goodness-of-fit | Test if observed distribution matches expected frequencies. | Full |
| Fisher's exact test | Exact test for 2x2 contingency table; alternative: one/two-sided. | Full |
| Pearson correlation matrix | Pairwise Pearson/Spearman/Kendall correlations with p-values; listwise NA handling. | Full |
| Correlation (pairwise) | Single correlation with confidence interval (Fisher z-transform for Pearson). | Full |
| Partial correlation | Correlation between two variables controlling for others; corrected df. | Full |
| Effect size calculator | Cohen's d/Hedges' g, eta/omega-squared, Cramér's V, Fisher's f-squared. | Full |
| Power analysis | Sample size / power calculation for t-test, ANOVA, correlation, chi-square, regression. | Full |
| Sign test | Non-parametric paired/one-sample test; binomial test on signs of differences. | Full |
| Runs test | Wald-Wolfowitz test for randomness; dichotomize by median/mean or threshold. | Full |
| McNemar test | Change detection in paired binary outcomes; 2x2 table symmetry. | Full |
| Cochran Q test | Non-parametric repeated measures for binary outcomes (3+ measurements). | Full |
| Jonckheere-Terpstra test | Non-parametric alternative to ANOVA for ordered groups (monotonic trend). | Full |
| Kendall's W | Coefficient of concordance for 2+ judges/raters across multiple items. | Full |
| Kendall's tau | Rank correlation between two variables (tau-b for ties). | Full |
| Mood's median test | Non-parametric k-group test based on median; robust to outliers. | Full |
| Permutation test | Permutation-resampling test (10000 permutations); mean difference or custom statistic. | Full |
| Kernel density estimate | Non-parametric density estimation; Scott/Silverman bandwidth, Gaussian kernel. | Full |
| MANOVA | Multivariate analysis of variance; Wilks' lambda, Pillai, Hotelling-Lawley, Roy's root. | Full |
| Distribution fitting + GOF | Fit Exponential-Family and related distributions (gamma, exponential, lognormal, Weibull, normal) by MLE, rank by AIC, and test fit with Kolmogorov-Smirnov. Matches R MASS::fitdistr + ks.test. | Full |
Statios's model namespace covers 50+ regression and generalized linear estimation procedures, from OLS to latent-variable extensions. Methods range from standard parametric models (linear, logistic, count) to specialized estimators for endogenous/selected samples, censoring, and complex errors. All predictor-taking handlers auto-expand categorical predictors R-style (labeled indicator columns, named reference level, factors field) — no manual dummy coding required.
| Procedure | What it does | Status |
|---|---|---|
| Linear Regression (OLS) | Standard ordinary least squares with diagnostics (R², F-test, VIF, Durbin-Watson, residual normality). Categorical predictors are auto-expanded R-style (labeled indicators, named reference level, factors field) — no manual dummy coding required. | Full |
| Linear Regression — Robust SEs (HC0–HC3) | OLS with heteroskedasticity-consistent standard errors; selectable HC0 (White), HC1 (Stata default), HC2, or HC3 (MacKinnon & White 1985). | Full |
| Linear Regression — Newey-West HAC SEs | OLS with Newey-West heteroskedasticity- and autocorrelation-consistent (HAC) standard errors (Bartlett kernel, set hac_lags); standard for time-series regressions. | Full |
| Errors-in-Variables Regression | Method-of-moments correction for classical measurement error (attenuation bias) using per-regressor reliability ratios; reports naive OLS vs corrected coefficients. | Full |
| SIMEX (measurement-error correction) | Corrects attenuation bias from a regressor measured with known additive error variance by simulation-extrapolation: adds increasing extra noise, refits OLS at each lambda (averaging over B Monte-Carlo replicates), and extrapolates the coefficient quadratically back to lambda=-1 to recover the error-free estimate. | Full |
| Frisch-Waugh-Lovell Partial Slope | Partials the controls out of both Y and the focal regressor and regresses the residuals through the origin; numerically equals the focal coefficient in the full OLS, with the partial correlation. | Full |
| Omitted-Variable Bias Decomposition | Decomposes omitted-variable bias for each included regressor into short-model vs long-model coefficients and the exact gamma*delta contribution of each omitted variable. | Full |
| Log-Y Retransformation (Duan Smearing) | Fits a log-scale OLS and reports Duan's smearing factor S=mean(exp(residuals)) plus naive vs bias-corrected (smeared) retransformed predictions on the original scale. | Full |
| Potential Outcomes (ATE/ATT/ATU) | Reports the average treatment effect (ATE), the effect on the treated (ATT) and on the untreated (ATU) in Rubin's counterfactual notation by regression adjustment under unconfoundedness: an OLS fit of the outcome on treatment, covariates and their interactions predicts each unit's pair (Y(1), Y(0)), with delta-method or bootstrap standard errors. | Full |
| Selection Model (MNAR missingness) | Heckman (1979) two-step selection model for non-ignorable (MNAR) missingness: a probit models whether the outcome is observed, the outcome equation is corrected for selection on unobservables via the inverse Mills ratio, and rho (with its rho=0 ignorability test) quantifies the selection correlation, reported alongside naive complete-case OLS for comparison. | Full |
| Pattern-Mixture Model (MNAR) | Pattern-mixture model (Little 1993) for non-ignorable (MNAR) missing outcomes: partitions the sample by the outcome's missingness, fits the completers' regression, imputes the missing outcomes under a sensitivity restriction whose delta shifts the missing-group mean (delta=0 = MAR), and reports the pattern-averaged mean, fraction missing, and a sensitivity curve. | Full |
| Continuous-Treatment Dose-Response (GPS) | Estimates the average dose-response function of a continuous treatment with the Hirano-Imbens generalized propensity score: models the treatment given covariates as Normal, regresses the outcome on a flexible function of the treatment and the GPS, and averages to trace E[Y(t)] over a grid of doses, returning the dose-response curve, the GPS-adjusted average marginal effect, and the naive unadjusted slope for comparison. | Full |
| Abadie Kappa-Weighting (compliers) | Uses Abadie's (2003) kappa weights kappa = 1 - D(1-Z)/(1-p(X)) - (1-D)Z/p(X) to estimate the complier share, the complier mean outcome, the mean covariates among compliers versus the full sample, and a kappa-weighted LATE in a binary-instrument / binary-treatment IV setting. | Full |
| Marginal Treatment Effect (MTE) | Estimates the Heckman-Vytlacil marginal treatment effect via local IV / generalized Roy: fits the propensity score and a flexible control function in p, differentiates to trace the MTE over the unobserved-resistance dimension, integrates it with the appropriate weights to recover ATE/ATT/ATUT/LATE, and tests for essential heterogeneity. | Full |
| Synthetic DiD | Synthetic Difference-in-Differences (Arkhangelsky et al. 2021) combines synthetic-control unit weights with difference-in-differences time weights to estimate the ATT on a balanced treated/control panel, reporting the SDID effect, a placebo/jackknife standard error, the sparse non-negative unit weights and the pre-treatment fit. | Full |
| Changes-in-Changes (Athey-Imbens) | Nonlinear generalization of difference-in-differences (Athey & Imbens 2006) that maps the treated-pre outcome distribution through the control group's pre-to-post distributional change to identify the entire counterfactual treated distribution, reporting the ATT plus quantile treatment effects (0.25/0.5/0.75) that reveal heterogeneous effects ordinary DiD would miss. | Full |
| Doubly-Robust DiD (Sant'Anna-Zhao) | Estimates the ATT in a 2x2 difference-in-differences design by combining an outcome-regression DiD with an inverse-propensity-weighted DiD into the doubly-robust estimator of Sant'Anna & Zhao (2020), which stays consistent if either the propensity-score model or the untreated-outcome model is correct, reports influence-function standard errors with the IPW and OR components and the simple DiD for contrast, and reduces exactly to the canonical mean 2x2 DiD when no covariates are supplied. | Full |
| Honest DiD (parallel-trends sensitivity) | Rambachan & Roth (2023) sensitivity analysis for difference-in-differences: estimates the event-study coefficients, then bounds the post-treatment effect under a relative-magnitudes restriction (Mbar) on how far post-treatment trend violations can exceed observed pre-trends, reporting robust confidence sets across Mbar and the breakdown Mbar at which significance is lost. | Full |
| Sun-Abraham Event Study | Interaction-weighted (IW) event-study estimator that saturates the model with cohort-by-relative-period interactions and aggregates them by cohort shares, recovering the true dynamic treatment path (and clean pre-trends) under staggered adoption with heterogeneous effects where conventional dynamic TWFE is contaminated. | Full |
| Callaway-Sant'Anna Staggered DiD | Callaway & Sant'Anna (2021) staggered difference-in-differences estimating group-time average treatment effects ATT(g,t) via clean 2x2 DiD against not-yet-treated/never-treated comparison units, aggregated to an overall ATT, an event-study (dynamic) profile, and per-cohort effects, with a naive two-way FE benchmark to expose heterogeneous-timing bias. | Full |
| Causal Forest (heterogeneous effects) | Honest generalized random forest (Wager-Athey) that estimates the conditional average treatment effect tau(x) via split-sample honest trees with R-learner local centering, returning the ATE with a forest-based SE, the distribution of individual CATEs, and the leading covariate driving effect heterogeneity. | Full |
| Double / Debiased ML | Estimates a debiased treatment effect in the partially-linear model Y = theta*D + g(X) + e by cross-fitting gradient-boosted nuisance regressions for E[Y|X] and E[D|X] and solving the orthogonal (Neyman) moment on the residuals, reporting theta with robust SE/CI alongside the biased naive single-regression estimate for contrast. | Full |
| McCrary Density Test (RD manipulation) | McCrary (2008) density-discontinuity test that fits separate triangular-kernel local-linear density estimates on each side of an RD cutoff and tests whether the log-density of the running variable jumps at the threshold, detecting sorting/manipulation that would invalidate the RD design. | Full |
| Propensity-Score Subclassification | Estimates the ATE and ATT of a binary treatment by fitting a propensity-score logit, splitting units into quantile strata of the score, and combining the within-stratum treated-minus-control mean differences weighted by stratum size (ATE) or treated share (ATT), with per-stratum effects and a stratified standard error. | Full |
| Mahalanobis Matching (ATT) | Nearest-neighbour matching estimator of the Average Treatment effect on the Treated that matches each treated unit to its closest control(s) in Mahalanobis covariate distance (pooled-control inverse covariance), imputes the counterfactual from the matched controls, and reports the ATT with an approximate Abadie-Imbens standard error and covariate balance. | Full |
| Box-Cox Transformed Regression | Estimates the Box-Cox power-transform lambda maximizing the concentrated profile log-likelihood, then fits OLS on the transformed (strictly-positive) response. | Full |
| Stochastic Frontier Analysis | Maximum-likelihood Aigner-Lovell-Schmidt stochastic frontier (half-normal inefficiency) for production or cost functions, returning frontier coefficients, sigma_v/sigma_u, lambda, gamma, mean technical efficiency E[exp(-u)] and per-observation efficiency scores. | Full |
| Finite-Mixture Regression (latent class) | Fits a finite mixture of K linear regression regimes by EM, recovering each latent class's own coefficient vector, error variance, and mixing proportion together with the log-likelihood, BIC, and posterior class-assignment summary. | Full |
| Dirichlet-Process Mixture Density | Bayesian nonparametric density estimation that fits an infinite (Dirichlet-process) Gaussian mixture via truncated stick-breaking, automatically inferring the effective number of mixture components and returning their means, variances, weights, and the estimated density curve. | Full |
| Finite-Mixture Count (latent class) | Fits a finite mixture of K latent-class Poisson regressions by EM, recovering each regime's own log-link coefficient vector and mixing proportion together with the log-likelihood, BIC/AIC, and posterior class-assignment summary to capture unobserved heterogeneity and overdispersion in count data. | Full |
| Simultaneous Quantile Regression | Fits linear quantile regressions at several quantiles at once and uses a joint paired (xy) bootstrap covariance to report per-quantile coefficient tables and to test whether slopes change across the conditional distribution (e.g. tau=0.25 vs 0.75). | Full |
| Quantile Regression Forest | Estimates full conditional quantiles (Meinshausen 2006) with a random forest by reading the weighted empirical response distribution in each leaf, revealing heteroskedastic conditional intervals that widen with predictors plus an out-of-bag interval-coverage check and variable importance. | Full |
| Additive Quantile Regression (quantile GAM) | Fits a tau-quantile regression in which each predictor enters through a smooth cubic B-spline term, minimizing the asymmetric check-loss via linear programming to recover nonlinear conditional quantile curves. | Full |
| Composite Quantile Regression | Estimates a single shared slope vector by pooling the check-loss across an equally-spaced grid of quantiles (with quantile-specific intercepts), giving slope estimates that match OLS under Gaussian errors and beat it in efficiency under heavy-tailed errors, with bootstrap standard errors. | Full |
| Expectile Regression | Newey-Powell asymmetric-least-squares expectile regression, fit by iteratively reweighted least squares to estimate any conditional tau-expectile (tau=0.5 reproduces OLS exactly), with heteroskedasticity-consistent sandwich standard errors and an asymmetry interpretation. | Full |
| Quantile Count Regression (jittering) | Estimates conditional quantiles of an overdispersed count response by the Machado-Santos Silva (2005) jittering method -- uniform noise is added to the integer counts, quantile regression is run on the log-transformed scale, and coefficients are averaged over many jitter draws to give stable, monotone-in-tau multiplicative quantile effects. | Full |
| Generated-Regressor Inference (Murphy-Topel) | Two-step estimation that regresses a stage-2 outcome on the fitted values from a first-stage OLS, reporting both the naive stage-2 standard errors and the Murphy-Topel/Pagan-corrected standard errors that propagate the first-stage estimation uncertainty into the generated regressor. | Full |
| Heteroskedastic Probit | Harvey (1976) binary probit in which the latent-error standard deviation is modelled as sigma_i = exp(z_i'gamma), estimated by BFGS maximum likelihood, returning separate mean (beta) and variance (gamma) coefficient tables plus a likelihood-ratio test of homoskedasticity (H0: gamma=0). | Full |
| Kernel Regression (Nadaraya-Watson) | Nonparametric Nadaraya-Watson local-constant kernel smoother estimating the conditional mean of one outcome on one predictor (Gaussian or Epanechnikov kernel, Silverman's rule-of-thumb or user bandwidth), returning fitted values, a 100-point smooth curve, an R-squared-like fit measure, and the bandwidth used. | Full |
| Kernel Conditional Density | Estimates the full conditional density f(y|x) with a double Gaussian kernel (Nadaraya-Watson weighting in x combined with a kernel in y), returning conditional 0.1/0.5/0.9 quantile curves and the conditional mean as functions of x so heteroskedastic spread, skew and multimodality are visible where a mean regression cannot show them. | Full |
| Single-Index Model (Ichimura) | Semiparametric single-index regression y = g(x'beta) + e estimated by Ichimura's semiparametric least squares, recovering the index direction beta (identified up to scale, unit-norm normalized) by minimizing the leave-one-out kernel-regression residual sum of squares while leaving the link g(.) fully nonparametric, and reporting standardized and raw-scale index coefficients, the data-driven bandwidth, a leave-one-out pseudo-R-squared, and the estimated link curve. | Full |
| Maximum Score (Manski) | Manski's semiparametric maximum-score estimator for binary choice, recovering the scale-normalized coefficient direction by maximizing correctly-predicted signs of x'beta under only a conditional-median error restriction, staying consistent where logit/probit fail under heteroskedasticity or non-normal errors. | Full |
| Translog Production/Cost Function | Estimates a second-order flexible (transcendental logarithmic) production or cost function by OLS on log-inputs, their squares and pairwise cross-products, reporting output elasticities at the sample mean, returns-to-scale, and an F-test of the Cobb-Douglas (no-second-order-terms) restriction. | Full |
| Empirical Likelihood (mean) | Owen's nonparametric empirical-likelihood inference for a population mean or mean vector, returning the -2 log EL-ratio statistic with its chi-square p-value plus a distribution-free EL confidence interval that requires no normality assumption. | Full |
| Copula Fitting (bivariate dependence) | Fits Gaussian, Clayton, Gumbel and Frank copulas to two variables' rank pseudo-observations by maximum likelihood, reporting each family's parameter, log-likelihood and AIC, the AIC-selected copula, and Kendall's tau and Spearman's rho. | Full |
| Gaussian-Process Regression | Nonparametric Bayesian regression with an RBF + white-noise kernel that learns its length-scale and noise level by maximizing the log-marginal-likelihood, returning a smooth posterior mean plus per-point predictive uncertainty. | Full |
| BART (Bayesian Additive Regression Trees) | Flexible nonparametric regression that models the conditional mean as a regularized sum of trees fit by Bayesian backfitting MCMC, returning posterior-mean fitted values, predictive uncertainty, variable-inclusion importance, and in-sample R-squared. | Full |
| Gaussian-Process Classification | Fits a Gaussian-process classifier (RBF kernel, Laplace approximation) to a binary 0/1 outcome, reporting in-sample accuracy/log-loss/AUC, learned kernel hyperparameters, and a predicted-probability summary to capture smooth nonlinear class boundaries a linear logit would miss. | Full |
| k-Nearest-Neighbours Regression | Nonparametric regression that predicts each outcome as the average of its k nearest neighbours in standardized (Euclidean) predictor space, reporting an in-sample fit, a leave-one-out cross-validated RMSE and CV R-squared, and the chosen k. | Full |
| Spline Regression (B-splines) | Fits a flexible nonlinear curve by building a B-spline basis of one predictor (patsy bs() with given degrees of freedom and polynomial degree) and running OLS of the response on that basis, returning the fitted curve on a 100-point grid, the data-quantile knot locations, the basis dimension, R-squared and residual MSE. | Full |
| Generalized Additive Model (splines) | Fits y = intercept + sum_j f_j(x_j) + e with each term a penalized cubic B-spline (statsmodels GLMGam, Gaussian family), selecting the smoothing penalty by GCV and reporting per-term effective degrees of freedom, deviance, pseudo-R^2, and a fitted-value summary. | Full |
| GAM (Poisson / Logit) | Generalized additive model for count (Poisson/log) or binary (Binomial/logit) outcomes that fits each predictor as a penalized cubic B-spline smooth via penalized IRLS with GCV/UBRE smoothing selection, reporting per-smooth effective degrees of freedom, deviance explained, fitted curves with confidence bands, and approximate significance. | Full |
| Local-Linear Regression | Nonparametric degree-1 local-polynomial kernel smoother that fits a kernel-weighted least-squares line at each point and takes its intercept as the conditional-mean estimate (Gaussian or Epanechnikov kernel, Silverman rule-of-thumb or user bandwidth), correcting the boundary bias of Nadaraya-Watson and returning fitted values, a 100-point smooth curve, effective degrees of freedom, and an R-squared-like fit measure. | Full |
| Local Polynomial Regression | Nonparametric kernel-weighted local polynomial smoother of arbitrary degree that generalizes Nadaraya-Watson (degree 0) and local-linear (degree 1) fits, returning the fitted curve, its estimated first derivative, and a smoother effective-df fit measure, with higher degrees reducing boundary bias. | Full |
| Isotonic Regression (PAVA) | Nonparametric monotone least-squares fit of a response on a single predictor via the pool-adjacent-violators algorithm, returning the fitted step function (level sets), R^2, and the imposed direction. | Full |
| Wavelet Regression (denoising) | Nonparametric denoising of a response treated as a signal ordered by a predictor via discrete wavelet transform with soft-thresholding (VisuShrink universal or hybrid SureShrink), returning the denoised fitted curve, robust MAD noise estimate, retained-coefficient sparsity, and reconstruction diagnostics. | Full |
| Wavelet Coherence | Computes squared wavelet coherence R^2 in [0,1] between two time series across time and scale via a hand-rolled FFT-based Morlet continuous wavelet transform, smoothed cross-wavelet and power spectra (Torrence & Compo 1998; Grinsted et al. 2004), summarizing the dominant common period, coherence by short/medium/long scale bands, and the cone-of-influence-masked global coherence. | Full |
| Partially Linear (Robinson) | Robinson (1988) sqrt(n)-consistent semiparametric partially-linear regression y = x'beta + g(z) + e, estimating the linear slopes beta by the double-residual method (Nadaraya-Watson partialling-out of y and each x on z, then residual-on-residual OLS) while leaving g(z) fully nonparametric. | Full |
| Overdispersion Test (Cameron-Trivedi) | Cameron-Trivedi (1990) regression-based test of the Poisson equidispersion assumption: fits a Poisson GLM, then runs auxiliary through-origin OLS of the studentised squared-residual statistic on g(mu) for g(mu)=mu (NB1) and g(mu)=mu^2 (NB2), reporting per-variant alpha-hat, a one-sided t-test of H0 alpha=0, and an overdispersion verdict. | Full |
| Conway-Maxwell-Poisson Regression | COM-Poisson GLM (log link on lambda) with a jointly-estimated dispersion parameter nu that flexibly handles under- (nu>1) and over-dispersed (nu<1) counts, fit by maximum likelihood with a stable truncated normalizing constant and a Wald test of nu=1 against the Poisson. | Full |
| Endogenous Count (control function) | Poisson count regression with an endogenous regressor corrected by two-stage residual inclusion (control function): a first-stage OLS residual on instruments is added to the count model, yielding the endogeneity-corrected effect, a control-function residual coefficient that doubles as an endogeneity test, and bootstrap standard errors. | Full |
| System OLS (multi-equation) | Estimates a system of linear equations equation-by-equation by OLS (intercept added), reporting per-equation coefficient tables (coef/se/t/p/CI), R2 and n, plus the cross-equation residual covariance Sigma (symmetric, PSD) and residual correlation on the common listwise-complete sample - the SUR/FGLS baseline. | Full |
| Joint Confidence Region | Fits OLS and reports the joint F test that a chosen set of coefficients is simultaneously zero, the exact confidence-ellipse geometry (centre, covariance submatrix, eigen-axes and half-axis lengths) for exactly two coefficients, and Bonferroni simultaneous intervals, contrasting the tilted exact region with the conservative axis-aligned box. | Full |
| General GLS (specified Omega) | Feasible generalized least squares (Aitken) under a user-specified error covariance: AR(1) Toeplitz errors, groupwise heteroskedasticity (one variance per group level), or power heteroskedasticity (var proportional to |fitted|^delta), reporting the estimated structure parameter and an efficient coefficient table. | Full |
| Complementary Log-Log Regression | Binary regression with the asymmetric complementary log-log link P(y=1)=1-exp(-exp(x'beta)) for rare-event data and grouped/discrete-time proportional-hazards (survival) models, returning a coef/se/z/p/CI table with log-likelihood and AIC/BIC. | Full |
| GEE (Population-Averaged) | Generalized estimating equations fit a population-averaged (marginal) regression for clustered or longitudinal data, modelling within-cluster dependence through a working correlation (independence / exchangeable / AR-1) while reporting robust sandwich standard errors valid even under correlation misspecification. | Full |
| LIML (Limited-Information ML) | Limited-information maximum likelihood IV estimator that is less biased than 2SLS under weak or many instruments, reporting the k-class kappa eigenvalue, robust coefficient table with confidence intervals, first-stage instrument-strength diagnostics, and an overidentification test. | Full |
| Two-Sample IV (TS2SLS) | Two-sample two-stage least squares: estimates a single endogenous regressor's structural coefficient by fitting the first stage in one half of the data and carrying the fitted endogenous value into the reduced-form/structural equation in the other half (Inoue-Solon TS2SLS; Angrist-Krueger TSIV design), reporting the coefficient with a robust SE, first-stage strength, and sample sizes. | Full |
| Semiparametric IV (sieve) | Newey-Powell / Ai-Chen series (sieve) two-stage instrumental-variables estimation of an unknown dose-response g(endog) approximated by a polynomial sieve and instrumented by a sieve basis of the instruments, returning the estimated g(.) curve on a grid, the average derivative, HC1-robust standard errors, first-stage relevance, and a naive sieve-OLS comparison to expose endogeneity bias. | Full |
| Lewbel Heteroskedasticity IV | Estimates a regression with an endogenous regressor when no external instruments exist, by constructing Lewbel (2012) generated instruments from heteroskedasticity in the first-stage residual and running heteroskedasticity-robust 2SLS, reporting the first-stage F and an OLS-vs-Lewbel comparison. | Full |
| Generalized Empirical Likelihood (GEL) | Information-theoretic estimation of a linear IV / moment-condition model via the GEL saddle-point (empirical likelihood, exponential tilting, or continuous-updating), returning coefficients with sandwich SEs, an LR-type overidentification test, and nonnegative implied probabilities; reproduces 2SLS exactly when just-identified. | Full |
| Empirical-Likelihood Regression | Estimates linear-regression coefficients on the OLS estimating-equation moments E[x(y - x'beta)]=0 and tests/intervals them via Owen's empirical likelihood, giving distribution-free, generally-asymmetric EL confidence intervals plus chi-square-calibrated EL-ratio p-values whose point estimates coincide with OLS. | Full |
| Continuously-Updated GMM (CUE) | Continuously-updated GMM for a linear IV model that re-estimates the optimal weight matrix as a function of the parameters inside the objective and minimises jointly, returning CUE coefficients with robust standard errors, z/p-values, the Hansen J overidentification test, and (when just-identified) an exact 2SLS match. | Full |
| Optimal-Instrument GMM | Efficient GMM for a linear IV model that builds Chamberlain (1987) optimal instruments A(z)=E[x|z]/Var(e|z) from a flexible projection of the regressors on the instruments plus a fitted conditional-variance model, reporting beta with efficient-GMM standard errors, first-stage fit, Hansen's J overidentification test, and an efficiency comparison versus plain 2SLS. | Full |
| Nonlinear GMM (exponential moments) | Two-step efficient GMM for the exponential / multiplicative-error model y = exp(x'beta)*eta with instruments, using Mullahy's multiplicative moment E[z*(y*exp(-x'beta)-1)]=0 to identify possibly-endogenous regressors (a nonlinear analogue of 2SLS) and reporting the coefficient table plus Hansen's J overidentification test. | Full |
| Method of Simulated Moments | Method of Simulated Moments / indirect inference for a univariate AR(1)-plus-measurement-noise model whose likelihood is intractable: parameters (phi, sigma_eta, sigma_e) are chosen so simulated auxiliary statistics (variance and first two autocovariances) match the data's, minimizing a scale-free weighted distance with common random numbers and reporting estimates, sandwich SEs with the (1+1/S) simulation-noise inflation, the objective value, and the moment-match table. | Full |
| Minimum Distance (CMD) | Classical minimum-distance (minimum-chi-square) estimation that recovers a structural IV slope from the OLS reduced-form coefficients by minimizing (pi_hat - h(theta))' W (pi_hat - h(theta)) with the efficient weight W = inv(Var(pi_hat)), reporting theta_hat, CMD standard errors, the overidentification chi-square, and (in the just-identified case) exact agreement with 2SLS. | Full |
| IV Quantile Regression (Chernozhukov-Hansen) | Estimates the structural effect of an endogenous regressor on a chosen conditional quantile of the outcome using one or more instruments, via the Chernozhukov-Hansen inverse-quantile-regression grid search that drives the instrument coefficient to zero, correcting the endogeneity bias that contaminates ordinary quantile regression. | Full |
| Oaxaca-Blinder Decomposition | Decomposes the mean outcome gap between two groups from separate within-group OLS fits into the threefold endowments/coefficients/interaction components and the twofold pooled-reference (Neumark) explained vs unexplained split. | Full |
| Prais-Winsten AR(1) Regression | Iterative AR(1) FGLS that quasi-differences the data and retains a scaled first observation, correcting OLS for first-order serial correlation; reports rho and before/after Durbin-Watson. | Full |
| Linear Probability Model | OLS of a binary 0/1 outcome with HC1 robust SEs; coefficients are marginal effects on P(y=1), and the share of out-of-[0,1] fitted values is flagged. | Full |
| Generalized Linear Model (family x link) | Unified GLM: choose the exponential family (gaussian, poisson, binomial, gamma, inverse-Gaussian) and link (identity, log, logit, probit, inverse, sqrt). Reports coefficients, deviance, AIC/BIC. | Full |
| Linear Combination of Coefficients (lincom) | Tests an arbitrary linear combination g'β of OLS coefficients with its standard error, t, p-value, and 95% CI (Stata-style lincom). | Full |
| Delta-Method (nonlinear combination) | Delta-method standard error for a sum / difference / ratio / product of two OLS coefficients, with t, p-value, and 95% CI. | Full |
| Restriction Test (F / Wald / LR) | General joint-exclusion test: F, Wald, and likelihood-ratio statistics for dropping a subset of regressors (unrestricted vs restricted OLS). | Full |
| Binary Logistic Regression | Binary outcome (0/1) modeled as probability; reports odds ratios, McFadden R², classification accuracy. | Full |
| Probit Regression | Binary choice via normal CDF link; includes marginal effects and classification accuracy. | Full |
| Multivariate Probit | Jointly models 3+ correlated binary outcomes by maximum simulated likelihood with the GHK simulator, returning per-equation probit coefficients plus the full cross-equation error correlation matrix. | Full |
| Bivariate Probit | Jointly estimates two binary-outcome probit equations with correlated normal errors by maximizing the bivariate-normal log-likelihood, reporting both coefficient tables, the error correlation rho with a Wald rho=0 test that detects whether the outcomes can be modeled as two independent probits, the log-likelihood and n. | Full |
| Ordered Logit | Ordinal outcome; reports coefficients, threshold parameters, and McFadden pseudo-R². | Full |
| Generalized Ordered Logit | Fits an ordinal-outcome model that relaxes the proportional-odds (parallel-lines) assumption by letting each cumulative split have its own ML-estimated coefficient vector, reporting per-threshold coefficient tables, a Brant-style Wald test (with an LR cross-check) of parallel lines, log-likelihood, AIC/BIC, and an interpretation. | Full |
| Sequential / Continuation-Ratio Logit | Models an ordinal outcome as a sequence of binary continuation-ratio logits -- one per ordered transition, fit on the at-risk subset {Y>=r} -- yielding stage-specific coefficients for outcomes whose categories are stages literally passed through in order. | Full |
| Ordered Probit | Ordinal outcome via normal CDF; threshold parameters and pseudo-R² included. | Full |
| Multinomial Logit | Multi-category outcome; reports coefficients and relative-risk ratios per category relative to base. | Full |
| Choice-Based Sampling (WESML) | Corrects a conditional/multinomial logit for endogenous (choice-based) stratified sampling using the Manski-Lerman WESML estimator, reweighting each observation by population-share / sample-share of its chosen alternative to recover consistent population coefficients with robust sandwich standard errors, alongside a side-by-side comparison to the biased unweighted fit. | Full |
| Multinomial Probit (GHK) | Estimates a random-utility multinomial probit on long-format choice data by maximum simulated likelihood with the Geweke-Hajivassiliou-Keane (GHK) recursive simulator, recovering attribute coefficients and the identified correlated-error covariance while relaxing the IIA restriction of the conditional logit. | Full |
| Nested Logit | Two-level nested logit for discrete choice estimated by FIML, relaxing IIA within nests via per-nest dissimilarity (lambda) parameters with a 0<lambda<=1 RUM-consistency check and a likelihood-ratio test against the conditional logit. | Full |
| Mixed (Random-Parameters) Logit | Random-parameters (mixed) logit estimated by simulated maximum likelihood on long-format discrete-choice data, drawing Normal mixing coefficients via Halton draws to recover preference means and the std devs that quantify unobserved heterogeneity. | Full |
| Maximum Simulated Likelihood | Fits a random-coefficient (mixed) binary-choice probit by Maximum Simulated Likelihood, approximating the intractable integral over unobserved slope heterogeneity with shifted-Halton low-discrepancy draws and reporting the mean coefficients, the heterogeneity standard deviation, the simulated log-likelihood, the number of draws, and a likelihood-ratio comparison to the pooled (no-heterogeneity) probit. | Full |
| Conditional Logit (McFadden) | Estimates McFadden's choice-specific conditional logit on long-format discrete-choice data, recovering how alternative-varying attributes drive the probability that each alternative is chosen within a choice occasion, with a full coefficient table, log-likelihood, and McFadden pseudo-R2. | Full |
| Poisson Regression | Count data (non-negative integer); reports incidence-rate ratios (IRRs) and dispersion diagnostics; optional exposure/offset term for rate (frequency) models. | Full |
| Negative Binomial (NB1 / NB2) | Overdispersed count data; MLE-estimated dispersion (alpha). Selectable NB2 (quadratic variance, default) or NB1 (linear variance). Includes likelihood-ratio test of Poisson vs NB. | Full |
| Zero-Inflated Poisson (ZIP) | Count data with excess zeros via mixing; separate count and zero-inflation equations with Vuong test. | Full |
| Zero-Inflated Negative Binomial (ZINB) | Overdispersed count data with excess zeros; count + inflation + alpha parameter estimated jointly. | Full |
| Hurdle Model | Two-part: logit for zero vs nonzero + truncated Poisson for positive counts. | Full |
| Tobit Regression (Censored) | Outcome censored at known bound (lower/upper); MLE estimation with sigma parameter. | Full |
| Censored LAD (Powell CLAD) | Powell's (1984) Censored Least Absolute Deviations estimator for left-censored (Tobit-type) outcomes, fit by an iterated median-regression linear-programming scheme with bootstrap standard errors, consistent under conditional-median restrictions and robust to heteroskedastic or heavy-tailed errors where Tobit is biased. | Full |
| Truncated Regression | Sample truncated (observations above threshold retained); MLE with sigma. | Full |
| Heckman Selection Model (Two-Step) | Endogenous sample selection via probit + IMR augmentation; reports rho correlation coefficient. | Full |
| Endogenous Switching (Roy model) | Estimates a two-regime Roy switching regression with regime-specific outcome equations and a probit selection equation, correcting each regime for selection on unobservables via its own inverse Mills ratio and reporting the rho_0/rho_1 error-correlation terms. | Full |
| Beta Regression | Outcome in (0,1); precision parameter (phi) and mean-model coefficients estimated jointly. | Full |
| Fractional-response quasi-MLE (Papke-Wooldridge) | Outcome in [0,1]; Papke-Wooldridge (1996) Bernoulli quasi-MLE — GLM with binomial family + logit link; includes marginal effects. | Full |
| Quantile Regression | Conditional quantile (median or specified tau); pseudo-R² and quantile-specific inference. | Full |
| Ridge Regression (L2) | L2 regularized OLS on standardized predictors; alpha controls shrinkage intensity. | Full |
| LASSO Regression (L1) | L1 regularized OLS; automatic feature selection via coefficient sparsity. Standardized fit. | Full |
| Elastic Net (L1+L2) | Combined L1/L2 penalty on standardized predictors; l1_ratio balances penalties. | Full |
| Elastic Net (cross-validated) | Elastic-net penalized linear regression that standardizes predictors and uses K-fold cross-validation to pick the penalty strength lambda at minimum CV-MSE and at the 1-SE rule, reporting the CV-MSE curve, original-scale selected coefficients, the number selected, and R^2. | Full |
| Robust Regression (Huber M-estimator) | M-estimator resistant to outliers; asymptotic-normal Wald inference (z-stat); no t-distribution df. | Full |
| Stepwise Selection | Greedy forward/backward/both; adds/removes by AIC or BIC until no improvement. | Full |
| Weighted Least Squares (WLS) | User-supplied or estimated weights (FGLS); corrects heteroscedasticity. | Full |
| Feasible Generalized Least Squares (FGLS) | Cochrane-Orcutt iterative GLSAR; corrects serial correlation in errors. | Full |
| Nonlinear Least Squares (NLS) | User-provided formula and starting values; scipy.optimize curve_fit minimizes residual sum of squares. | Full |
| Seemingly Unrelated Regressions (SUR) | Multiple equations fitted jointly accounting for cross-equation correlation; Breusch-Pagan test included. | Full |
| Three-Stage Least Squares (3SLS) | System of equations with endogenous right-hand-side variables; IV + SUR combined. | Full |
| FIML (simultaneous system) | Full-Information Maximum Likelihood joint estimation of a two-equation linear simultaneous-equations system, maximizing the system Gaussian log-likelihood concentrated over the residual covariance (with the ln|det(B)| Jacobian) to recover structural coefficients with FIML standard errors, the residual covariance/correlation, the system log-likelihood, and a side-by-side comparison against equation-by-equation 2SLS. | Full |
| Generalized Method of Moments (GMM) | IV-GMM with moment conditions; J-test of overidentifying restrictions. | Full |
| Panel Fixed Effects | Within-entity estimator; removes time-invariant heterogeneity. Clustered SEs on entity. Optional two-way (entity + time) effects. | Full |
| Panel Random Effects | GLS random-effects estimator; assumes random effect uncorrelated with predictors. | Full |
| Linear Mixed Model (Random Intercepts/Slopes) | REML by default; fixed-effect Wald-z inference. AIC/BIC reported only under ML. | Full |
| Intraclass Correlation (ICC) | ICC(1) from one-way random-intercept model; variance-partition coefficient for clustering. | Full |
| Difference-in-Differences (DiD) | Treatment×Post interaction; cluster-robust or HC1 SEs depending on clustering. | Full |
| Regression Discontinuity (Sharp RDD) | Local-linear at cutoff with triangular or uniform kernel. Ad-hoc or user-supplied bandwidth. | Limited |
| RD Robust (CCT bias-corrected) | Sharp regression-discontinuity treatment effect via local-polynomial (triangular-kernel) fits on each side of the cutoff within an MSE-optimal bandwidth, reporting the conventional point estimate, the Calonico-Cattaneo-Titiunik (2014) bias-corrected estimate, conventional and robust standard errors, confidence intervals, and effective sample sizes. | Full |
| Propensity Score Matching (PSM) | Logit/probit propensity score; 1:1/1:k matching with replacement or caliper. | Full |
| Extended Regression Model — Linear (ERM-Linear) | Combines endogeneity (IV-2SLS), sample selection (Heckman), treatment effects in one model. | Full |
| Extended Regression Model — Probit (ERM-Probit) | Endogeneity (control function) + selection (bivariate-probit IMR) for binary outcome. | Limited |
| Extended Regression Model — Ordered (ERM-Ordered) | Ordered probit with sample selection correction via IMR augmentation. | Limited |
| Extended Regression Model — Interval (ERM-Interval) | Outcome observed as bounds [lower, upper]; handles left/right/interval censoring via MLE. | Full |
| ERM Diagnostics | Specification tests for endogeneity, selection bias, and treatment effect significance within ERM. | Full |
| LOESS Regression | Local polynomial regression; non-parametric smoothing via weighted least squares. | Full |
| Gamma GLM (severity) | Gamma generalized linear model with log link for strictly-positive responses (e.g. claim severity/cost); supports an exposure/offset term. Dispersion via Pearson, matching R glm(family=Gamma(link="log")). | Full |
| Tweedie GLM (pure premium) | Tweedie compound Poisson-Gamma GLM (log link) for non-negative data with a mass at zero — single-model pure-premium pricing; configurable variance power p in (1,2). | Full |
Statios provides 31 post-estimation and regression diagnostic procedures spanning heteroskedasticity tests, serial correlation diagnostics, influence measures, marginal effects, contrasts, and model comparison tools.
| Procedure | What it does | Status |
|---|---|---|
| Normality tests (comprehensive suite) | Shapiro-Wilk, Kolmogorov-Smirnov, D'Agostino-Pearson, Anderson-Darling; includes skewness/kurtosis and Q-Q plot data. | Full |
| Homogeneity of variance test | Levene and Bartlett tests for equality of variances across groups; concludes on Levene (robust). | Full |
| Outlier detection (multiple methods) | IQR, Z-score (|z|>3), and modified Z-score (MAD-based) methods with index identification. | Full |
| Multicollinearity check | Variance Inflation Factor (VIF) analysis with concern classification (low/moderate/high). | Full |
| Automatic test suggestion | Recommends appropriate statistical tests based on data characteristics. | Full |
| Breusch-Pagan test for heteroskedasticity | LM and F variants test if residual variance depends on predictors; requires n > k+2. | Full |
| White test for heteroskedasticity | General heteroskedasticity test not assuming specific form; more flexible than Breusch-Pagan. | Full |
| Goldfeld-Quandt heteroskedasticity test | Sorts by first predictor, splits at the midpoint, drops a block of observations starting at the midpoint, and F-tests the lower vs upper group residual variances. | Full |
| Breusch-Godfrey serial correlation test | LM and F tests for autocorrelation up to specified lags in OLS residuals. | Full |
| Durbin-Watson autocorrelation statistic | DW near 2 indicates no autocorrelation; <1.5 positive, >2.5 negative autocorrelation. | Full |
| Ljung-Box autocorrelation test | Tests joint significance of autocorrelations at specified lags; generic for series/residuals. | Full |
| Heteroskedasticity-robust LM test | Wooldridge robust LM test that a set of regressors are jointly zero, valid under heteroskedasticity of unknown form (n − SSR auxiliary regression on residualized products). | Full |
| AR(1) serial-correlation test | Wooldridge's t-test for first-order serial correlation: regress OLS residuals on their own lag (plus the regressors) and test ρ = 0. | Full |
| Durbin's h Test | Durbin's h test for first-order serial correlation in OLS models that include a lagged dependent variable, where the Durbin-Watson statistic is biased toward 2. | Full |
| Moulton Factor (clustered SE inflation) | Moulton (1986) standard-error inflation factor: estimates the residual intra-class correlation across groups and reports naive OLS vs Moulton-adjusted standard errors. | Full |
| Vuong Test (non-nested) | Vuong's (1989) likelihood-ratio test for two non-nested models (OLS/logit/Poisson), comparing per-observation log-likelihoods to decide which specification is closer to the true data-generating process, with AIC/BIC complexity-corrected variants. | Full |
| Cox Test (non-nested) | Pesaran-Cox test discriminating between two non-nested linear regression models via the difference in log-likelihoods adjusted by its expectation under each model, reported in both directions as standard-normal statistics with a preferred-model verdict. | Full |
| Encompassing Test (non-nested) | Mizon-Richard (1986) encompassing test that asks, by artificial nesting, whether one non-nested linear model explains everything its rival adds: each direction adds the rival's non-overlapping regressors to the base model and F-tests their joint significance (insignificant => the base model encompasses the rival), returning F/df/p, per-direction verdicts, and a preferred model. | Full |
| J Test (non-nested models) | Davidson-MacKinnon J test that discriminates between two non-nested linear specifications by adding each model's OLS fitted values to the other and testing their t-statistic in both directions, returning per-direction rejection verdicts and a preferred model. | Full |
| Information-Matrix Test (White) | White's (1982) omnibus information-matrix specification test for OLS: IM = n*R^2 of the auxiliary regression of squared residuals on the unique squares and cross-products of the regressors (chi-square), detecting general misspecification — wrong functional form, heteroskedasticity, or non-normality — not just heteroskedasticity. | Full |
| Harvey Multiplicative Heteroskedasticity | Harvey's (1976) LM test for multiplicative heteroskedasticity, regressing the log of squared OLS residuals on chosen variables to estimate the variance model Var(e)=exp(z'alpha) and test whether the variance depends on them. | Full |
| LM / Score Test (omitted regressors) | Classic Lagrange-multiplier (score) test for omitted regressors: regresses the residuals of the restricted OLS (dependent on kept) on the full regressor set and reports LM = n*R-squared of that auxiliary regression as a chi-square(q) test that the tested coefficients are jointly zero, with the equivalent F-form and a reject/not verdict. | Full |
| Multivariate Normality (Doornik-Hansen) | Tests whether a set of variables (or VAR residuals) is jointly multivariate normal via Mardia's multivariate skewness and kurtosis statistics and the Doornik-Hansen omnibus chi2(2p) test, returning each statistic with its df and p-value, per-variable skew/kurtosis, and an overall normal/non-normal verdict. | Full |
| Ramsey RESET specification test | Tests for functional form misspecification by adding polynomial terms of fitted values. | Full |
| Hosmer-Lemeshow goodness-of-fit test | Groups logit predictions by deciles, compares observed vs expected event counts via chi-square. | Full |
| Variance Inflation Factor (VIF) analysis | VIF per predictor with concern thresholds (VIF>5 moderate, >10 high); includes mean VIF. | Full |
| Cook's distance influence measure | Identifies observations with disproportionate influence on regression; threshold default 4/n. | Full |
| Leverage (hat values) analysis | Detects high-leverage observations (unusual X values); threshold 2*(k+1)/n. | Full |
| DFBETAS parameter-specific influence | Measures change in each coefficient when one observation removed; threshold 2/sqrt(n). | Full |
| Marginal effects (logit/probit) | Average Marginal Effects (AME) or Marginal Effects at Means (MEM) for binary choice models. | Full |
| Predicted probabilities with CI | Logit/probit predictions on observed data or at specified covariate values with 95% CI. | Full |
| Post-hoc multiple comparisons | Tukey, Bonferroni, Holm step-down, Scheffe, Games-Howell with p-value adjustments. | Full |
| Effect sizes across designs | Cohen's d, Glass's delta, Hedges' g, eta-squared, partial eta-squared, omega-squared, Cohen's f. | Full |
| Planned contrasts (pairwise/polynomial) | Pairwise, polynomial (Legendre), Helmert, deviation contrasts with t-tests and CI. | Full |
| Prediction intervals (OLS) | Confidence and prediction intervals for fitted values; wider than CI due to residual variance. | Full |
| Coefficient table (with VIF/robust SE) | Formatted OLS results with standardized betas, VIF, HC3 robust SE option; significance stars. | Full |
| Model comparison (R-sq, AIC, BIC) | Side-by-side OLS models with delta AIC/BIC relative to best; RMSE and F-stat per model. | Full |
| Marginal effects at specified values | Conditional marginal effects (OLS/logit/probit/Poisson) at user-supplied covariate values. | Full |
| Margins contrast (difference in effects) | Compare marginal effects of one predictor across two or more levels; delta-method SE. | Full |
| Conditional marginal effects (interaction) | Marginal effect of focal variable at 10th, 25th, 50th, 75th, 90th percentiles of moderator. | Full |
| Nested contrasts (hierarchical) | Two-sample Welch t-tests on specified pairs within a grouping variable. | Full |
| Elasticities (full/partial/arc) | dy/dx*x/y (full), dy/dx*x (partial), or arc elasticity across specified predictor values. | Full |
| Semi-elasticities (dy/dx/y and dy/dx*x) | Proportional change in y and linear change in y per unit change in x. | Full |
| Predicted means over groups (Stata-style) | Marginal cell means with 95% CI; pairwise comparisons across factor levels. | Full |
| Adjusted predictions (holding covariates constant) | Linear predictor at specified covariate values (mean/median/min/max); 95% CI included. | Full |
| Probability profile (logit/probit curve) | Binary outcome probability as function of one predictor; other predictors at mean. | Full |
| Treatment effect contrasts (ATE/ATT/ATC) | Average treatment effect via regression-based g-computation; balanced or flexible interaction. | Limited |
Comprehensive time series analysis, DSGE models, and advanced forecasting. Includes unit root tests, ARIMA/seasonal models, VAR/cointegration, impulse responses, nonlinear models, forecast evaluation, and mixed-frequency regression.
| Procedure | What it does | Status |
|---|---|---|
| Autocorrelation & Partial Autocorrelation (ACF / PACF) | Sample ACF and PACF (Durbin-Levinson / Yule-Walker) with white-noise confidence bands and per-lag Ljung-Box Q statistics, for ARMA order identification. | Full |
| HAC Long-Run Variance | Estimates the heteroskedasticity- and autocorrelation-consistent (HAC) long-run variance S = gamma_0 + 2*sum k(j/L)*gamma_j of a time series using the Bartlett (Newey-West) or Quadratic-Spectral (Andrews) kernel, with the Newey-West automatic plug-in bandwidth, reporting the variance ratio S/gamma_0 and the HAC standard error of the mean. | Full |
| Structural VECM (long-run restrictions) | Fits a Johansen VECM at a given cointegration rank and identifies structural shocks via long-run (Granger-representation) restrictions, reporting the reduced-rank long-run impact matrix, the lower-triangular structural long-run matrix (K-r permanent / r transitory shocks), and few-horizon structural impulse responses. | Full |
| Time-Varying-Parameter VAR | Estimates a VAR whose coefficients drift over time as random walks, cast as a linear-Gaussian state-space model and recovered by the Kalman filter and Rauch-Tung-Striebel smoother, returning smoothed coefficient paths (start/end/mean), state-innovation variances, an LR test of constant coefficients, and the log-likelihood. | Full |
| Markov-Switching VAR | Regime-switching vector autoregression (Hamilton) whose intercepts, dynamics, and innovation covariance switch between latent Markov regimes, estimated by EM (Hamilton filter + Kim smoother) returning per-regime VAR coefficients, the regime transition matrix, smoothed regime probabilities, and log-likelihood. | Full |
| Threshold VAR (TVAR) | Estimates a two-regime multivariate threshold VAR by grid-searching the threshold that splits the sample on a transition variable's lag, fits a separate VAR in each regime, and tests for threshold nonlinearity with a bootstrap-calibrated sup-LR statistic. | Full |
| Quantile VAR | Vector autoregression estimated at a chosen conditional quantile via equation-by-equation quantile regression of each variable on the full lagged vector, returning per-lag tau-quantile coefficient matrices alongside the mean (OLS) VAR for comparison, to capture tail/regime-dependent dynamics. | Full |
| Local Projections (Jorda IRF) | Estimates Jorda (2005) local-projection impulse responses by running a separate OLS regression of y_{t+h} on the shock at t (plus controls) at each horizon with Newey-West HAC standard errors, yielding an impulse-response function with confidence bands that is robust to dynamic misspecification relative to VAR IRFs. | Full |
| Unobserved Components (structural TS) | Harvey structural time-series model: decomposes a univariate series into stochastic level, optional local-linear-trend slope, optional stochastic seasonal, and irregular via Kalman-filter maximum likelihood, reporting component variances, smoothed series, log-likelihood/AIC, and a short forecast. | Full |
| Vector Moving-Average (VMA) | Fits a multivariate VMA(q) model y_t = mu + eps_t + Theta_1 eps_{t-1} + ... + Theta_q eps_{t-q} by conditional Gaussian maximum likelihood, returning the MA coefficient matrices, innovation covariance, finite (zero-beyond-lag-q) impulse responses, log-likelihood and information criteria. | Full |
| VARMA Model | Estimates a Vector ARMA(p,q) for a small multivariate time series by conditional maximum likelihood (via VARMAX), reporting the AR and MA coefficient matrices, residual covariance, log-likelihood and AIC/BIC. | Full |
| Intervention Analysis (interrupted TS) | Box-Tiao interrupted-time-series analysis fitting an AR(p) model with a deterministic step (permanent level shift) or pulse (transient shock) intervention dummy at a known date, reporting the estimated intervention effect with its t-test, the long-run steady-state level shift, pre/post means, and a plain-language interpretation. | Full |
| Subset / Restricted VAR | Estimates a vector autoregression then imposes data-driven zero restrictions on coefficients whose full-VAR |t| falls below a threshold, re-estimating the sparser system by restricted EGLS and reporting retained coefficients, the number of restrictions, and the SC/HQ/AIC improvement over the full VAR. | Full |
| Periodic VAR (seasonal coefficients) | Fits a periodic vector autoregression / periodic autoregression (PAR) in which the intercepts and lag coefficient matrices vary deterministically by season, returns the per-season coefficient matrices, and tests the constant-coefficient (standard-VAR) restriction via per-equation F tests and a system likelihood-ratio test of periodicity. | Full |
| Threshold Cointegration (Enders-Granger) | Tests for threshold cointegration with asymmetric error correction by fitting Enders-Granger TAR and momentum-TAR models to the cointegrating-residual changes, reporting the two regime adjustment speeds, the Phi no-cointegration statistic, and an F-test of symmetric vs. asymmetric adjustment. | Full |
| Generalized IRF (Koop-Pesaran-Potter) | Computes ordering-invariant generalized impulse-response functions for a VAR (Koop-Pesaran-Potter 1996; Pesaran-Shin 1998), tracing each variable's response to a shock in the chosen variable without imposing a recursive Cholesky ordering. | Full |
| LSTAR (Smooth-Transition AR) | Fits a logistic smooth-transition autoregression by nonlinear least squares, estimating two linear AR regimes plus the smoothness (gamma) and threshold (c) of a logistic transition over a lagged level, with the Luukkonen-Saikkonen-Terasvirta LM test of linearity against smooth-transition nonlinearity. | Full |
| Spectral Granger Causality (Geweke) | Decomposes Granger causality from one series to another by frequency using Geweke's (1982) spectral measure of linear feedback, reporting causality at low / business-cycle / high frequencies, the dominant frequency, and the frequency-averaged measure that matches the time-domain Granger test by Geweke's integral identity. | Full |
| Tsay Nonlinearity Test | Tsay (1986) F-test for threshold/quadratic nonlinearity in an AR(p) model: augments the linear AR with the squares and pairwise cross-products of the lagged values and tests their joint significance, returning the F statistic, degrees of freedom, p-value, and a linear/nonlinear verdict. | Full |
| BDS Test (nonlinearity) | Brock-Dechert-Scheinkman-LeBaron test for serial independence: computes the correlation-integral BDS statistic at embedding dimensions 2..max_dim and returns per-dimension N(0,1) statistics with p-values plus an overall iid / non-iid verdict, detecting both linear and nonlinear dependence (e.g. in model residuals). | Full |
| Kalman Smoother (state-space) | Fits a local-level (or local-linear-trend) unobserved-components state-space model by maximum likelihood and returns both the real-time Kalman-filtered and the full-sample RTS-smoothed level (and slope), with estimated irregular/level/slope variances, log-likelihood and smoothed-state standard errors. | Full |
| Time-Varying-Parameter Regression | Estimates regression coefficients that evolve over time as random walks via a linear-Gaussian state-space model fit by the Kalman filter/smoother, returning the smoothed time-varying coefficient paths, signal/state variances, and log-likelihood to detect structural change. | Full |
| IGARCH (Integrated GARCH) | Integrated GARCH(p,q) estimated by maximum likelihood with the variance persistence (sum of ARCH+GARCH coefficients) constrained to exactly 1, so volatility shocks are permanent rather than mean-reverting. | Full |
| GARCH-in-Mean (ARCH-M) | Estimates a GARCH-in-mean (ARCH-M) model in which the conditional volatility (std. dev.) or conditional variance enters the mean equation as a risk-premium term, reporting the lambda risk premium with significance alongside the omega/alpha/beta variance dynamics and persistence. | Full |
| FIGARCH (long-memory volatility) | Estimates a fractionally-integrated GARCH(p,d,q) by ML over a truncated ARCH-infinity expansion, recovering the long-memory parameter d in (0,1) that places volatility persistence between the GARCH (d=0) and IGARCH (d=1) extremes, with conditional-volatility summary, log-likelihood, AIC/BIC and a long-memory verdict. | Full |
| GARCH Portmanteau Diagnostics | Fits a GARCH(1,1) by Gaussian QMLE, forms standardized residuals, and tests them for remaining structure via Ljung-Box Q on z_t and z_t^2 and Engle ARCH-LM on z_t^2 at every lag, returning the variance parameters, per-lag statistics with p-values, and a pass/fail adequacy verdict for the volatility specification. | Full |
| Realized GARCH | Estimates the Realized GARCH(1,1) model of Hansen, Huang & Shek (2012), jointly fitting the variance recursion h_t = omega + beta*h_{t-1} + gamma*x_{t-1} and the measurement equation x_t = xi + phi*h_t + tau(z_t) + u_t by Gaussian QMLE, reporting omega/beta/gamma, measurement parameters, persistence (beta+gamma*phi), log-likelihood, and the fitted conditional-volatility path (building a realized proxy from squared returns when no realized measure is supplied). | Full |
| CCC Multivariate GARCH | Bollerslev (1990) Constant Conditional Correlation multivariate GARCH: fits a univariate GARCH(1,1) to each series, estimates the constant correlation matrix R from the standardized residuals, and forms conditional covariances H_t = D_t R D_t that are positive-definite by construction. | Full |
| HAR-RV (realized volatility) | Corsi (2009) Heterogeneous Autoregressive model of Realized Volatility: regresses next-period volatility on the daily (1-day), weekly (5-day) and monthly (22-day) trailing averages of a realized-volatility proxy, reporting component coefficients/SE/t/p, a joint significance F-test, R-squared and a one-step-ahead forecast. | Full |
| BEKK Multivariate GARCH | Fits a diagonal BEKK(1,1) multivariate GARCH (Engle & Kroner 1995) by Gaussian ML to 2-3 return series, estimating the diagonal A/B ARCH/GARCH parameters, implied per-series persistence, the time-varying conditional covariance/correlation path (guaranteed positive-definite at every t), the mean dynamic correlation, and the log-likelihood. | Full |
| Stochastic Volatility | Fits a stochastic-volatility model whose latent log-variance follows its own AR(1) state, estimated by Harvey-Ruiz-Shephard quasi-ML via the Kalman filter on log-squared returns, returning the persistence (phi), vol-of-vol (sigma_eta), long-run variance level (mu), and the Kalman-smoothed conditional-volatility path. | Full |
| Causality-in-Variance (Cheung-Ng) | Cheung-Ng (1996) test for volatility spillover between two series: GARCH-filters each series, then uses the n-scaled sum of squared cross-correlations of the squared standardized residuals to test, in each direction, whether past volatility of one series predicts the other's future volatility. | Full |
| FMOLS Cointegrating Vector | Phillips-Hansen Fully-Modified OLS estimates a single cointegrating vector among I(1) series, correcting OLS for serial correlation and regressor endogeneity via a Bartlett long-run covariance to yield an asymptotically efficient, unbiased long-run coefficient table with valid standard errors. | Full |
| DOLS Cointegrating Vector | Stock-Watson (1993) single-equation Dynamic OLS estimate of a cointegrating vector, regressing the I(1) dependent on the I(1) regressors plus leads and lags of their first differences, with HAC (Newey-West) standard errors on the asymptotically efficient long-run coefficients. | Full |
| Markov Chain (transition/stationary) | Estimates a first-order discrete Markov chain from a state sequence by maximum-likelihood transition counting, returning the row-normalized transition matrix, the stationary (invariant) distribution solving pi = pi P, the n-step transition matrix P^n, and Kac mean recurrence / mean first-passage times. | Full |
| Phillips-Ouliaris Cointegration Test | Residual-based test of the no-cointegration null among I(1) series: runs the cointegrating OLS, then applies the Phillips-Perron Z-alpha and Z-t unit-root statistics to the residuals (Newey-West long-run-variance corrected) against MacKinnon response-surface cointegration critical values to deliver Z-alpha, Z-t, an approximate p-value, and a cointegration verdict. | Full |
| Blanchard-Quah SVAR | Identifies permanent and transitory structural shocks in a bivariate (or n-variable) VAR via Blanchard-Quah long-run restrictions, returning the lower-triangular long-run impact matrix, the contemporaneous impact matrix, orthonormal structural impulse responses, and the forecast-error variance decomposition. | Full |
| VAR Residual Diagnostics (multivariate) | Fit a VAR(p) and run a battery of multivariate residual checks - the Hosking (1980) portmanteau/Ljung-Box test for serial correlation, a multivariate ARCH-LM serial-correlation test, and the Doornik-Hansen omnibus normality test - returning each statistic, df, p-value, and an overall model-adequacy verdict. | Full |
| VAR Lag-Order Selection (IC table + LR) | Fits a VAR(p) for every lag p=0..maxlags on a common fixed sample and reports the AIC, Hannan-Quinn, Schwarz/BIC and FPE information criteria plus a sequential likelihood-ratio test, returning each criterion's argmin and the LR-selected lag. | Full |
| ARCH-LM Test (ARCH effects) | Engle's LM test for conditional heteroskedasticity (volatility clustering): n·R² auxiliary regression of the squared series on its own lags. Run before fitting a GARCH model. | Full |
| Time-trend regression (detrending) | Regress a series on a polynomial deterministic time trend; reports trend coefficients, R², overall trend significance, and detrended-residual summary (trend- vs difference-stationary). | Full |
| Spurious-regression diagnostic | Regress y on x and flag the classic spurious-regression signature (high R², very low Durbin-Watson, non-stationary residuals via Engle-Granger ADF) for integrated, non-cointegrated series. | Full |
| Seasonal ARIMA (SARIMA) | Fit seasonal ARIMA with forecasting, including Ljung-Box and Jarque-Bera diagnostics. | Full |
| Holt-Winters Exponential Smoothing | Exponential smoothing with additive/multiplicative seasonal component and trend. | Full |
| Error-Trend-Seasonal (ETS) model | Automated error-trend-seasonal decomposition with AIC/BIC selection. | Full |
| Unobserved Components / State Space | Kalman filter state-space model with level, trend, and seasonal components. | Full |
| Structural VAR (SVAR) | Structural VAR with estimated A and B matrices for shock identification. | Full |
| Impulse Response Functions | Orthogonalized and non-orthogonalized IRF from VAR with cumulative responses. | Full |
| Forecast Error Variance Decomposition | Variance decomposition of VAR forecasts by shock source over horizons. | Full |
| Markov Switching Autoregression | Regime-switching model with time-varying transition probabilities and smoothed probabilities. | Full |
| Threshold Autoregression (TAR) | Nonlinear autoregression with endogenous threshold detection and linearity test. | Full |
| Dynamic Conditional Correlation GARCH | DCC-GARCH model for time-varying correlations (Engle 2002) via quasi-MLE. | Full |
| Zivot-Andrews unit root test | Unit root test with one endogenous structural break in level or trend. | Full |
| DF-GLS unit root test | Elliott-Rothenberg-Stock feasible GLS-detrended unit root test. | Full |
| ERS Point Optimal unit root test | Efficient detrending with GLS and point-optimal deviations under the null. | Full |
| Change-in-mean structural break detection | Detects shifts in the MEAN of one series via dynamic-programming least-squares (L2) segmentation, with the number of breaks chosen by BIC; reports segment means and variances. | Full |
| Quandt-Andrews Break Test (sup-Wald) | Tests for a single structural break at an unknown date in a linear regression by computing the Chow/Wald F for a full-coefficient break at every trimmed candidate point and reporting the sup-F (QLR), average-F and exp-F (Andrews-Ploberger) statistics, the estimated break fraction, Andrews (1993) asymptotic critical values, and a Hansen (1997) approximate p-value. | Full |
| CUSUM and CUSUM-squared stability test | Recursive residual stability test for parameter constancy over time. | Full |
| Particle Filter (SMC state-space) | Bootstrap particle filter (sequential Monte Carlo) for a univariate local-level state-space model: estimates the process/observation variances by maximum likelihood on the exact Kalman likelihood, then runs an SIR filter with systematic resampling to return the filtered latent-state mean, a 90% credible band, the one-step predictive log-likelihood, and the effective sample size over time, benchmarked against the exact Kalman filter. | Full |
| Koyck Distributed Lag | Estimates a Koyck (geometric) infinite distributed-lag model y_t = alpha + beta*x_t + lambda*y_{t-1} via OLS on the Koyck transform, reporting the impact multiplier, geometric decay rate, long-run multiplier beta/(1-lambda), mean/median lag, the implied lag-weight profile, and a Durbin's-h serial-correlation diagnostic. | Full |
| Toda-Yamamoto Granger causality | Granger causality test in levels with integration-order adjustment; Wald test on VAR lags. | Full |
| Nonlinear ARDL (NARDL) | Autoregressive distributed lag with asymmetric positive/negative decomposition; symmetry test. | Full |
| Pesaran-Shin-Smith ARDL bounds test | Cointegration test via F-test on lagged levels in error-correction form. | Full |
| Diebold-Mariano forecast comparison | Test comparing forecast accuracy of two models (MSE or MAE) with Newey-West variance. | Full |
| Forecast combination | Combine forecasts via equal weight, inverse MSE, OLS, or Bates-Granger method. | Full |
| Diffusion-index direct forecast (user-supplied factors) | Direct multi-step diffusion-index forecast y_{t+h} = a + sum g_p y_{t-p} + b'F_t using factor columns you supply (e.g. PCA component scores from analysis.pca), with an out-of-sample backtest against an AR benchmark. | Limited |
| Dynamic Factor Model (EM/Kalman) | Estimate a latent dynamic factor model on multiple standardized time series by EM with the Kalman filter/RTS smoother (Doz-Giannone-Reichlin / Watson-Engle), extracting common AR factors and reporting loadings, the smoothed factor path, factor AR coefficients, variance shares, log-likelihood and iteration count. | Full |
| Spectral analysis | Periodogram or Welch PSD with dominant frequency and period identification. | Full |
| Hamilton Filter (trend/cycle) | Hamilton (2018) regression-based business-cycle filter that decomposes a series into trend (the h-step-ahead forecast fitted from a constant and p lagged levels) and cycle (the forecast residual), reporting coefficients, R^2 and the cycle standard deviation as a sound, spurious-dynamics-free replacement for the Hodrick-Prescott filter. | Full |
| Spectral Coherence (cross-spectrum) | Estimates the squared coherency and phase spectrum between two time series via a smoothed Welch/averaged-periodogram cross-spectrum with a Hann taper, reporting coherence and phase by frequency, the peak-coherence frequency, and an approximate significance threshold. | Full |
| Unrestricted MIDAS regression | Mixed-frequency regression: unrestricted lags of high-frequency predictor on low-frequency outcome. | Limited |
| Almon polynomial MIDAS | MIDAS with polynomial lag weighting via NLS; information criterion comparison across lag lengths. | Limited |
| Beta polynomial MIDAS | MIDAS with beta lag structure for smooth, parsimonious high-frequency aggregation. | Limited |
| Exponential Almon MIDAS | MIDAS using exponential-Almon lag polynomial for flexible decay pattern. | Limited |
| MIDAS forecast | Forecast using fitted MIDAS model; extends from low-freq observations with high-freq predictors. | Full |
| MIDAS nowcasting | Real-time estimate of current low-frequency value using available high-frequency data. | Limited |
| MIDAS model comparison | Compare U-MIDAS, Almon, Beta, and Exponential MIDAS across lag range via information criteria. | Limited |
| Real Business Cycle model | Canonical RBC with technology shock; analytical steady state, IRF, variance decomposition. | Limited |
| New Keynesian 3-equation model | IS–Phillips–Taylor system; parameterizable demand, supply, monetary shocks with closed-form IRF. | Limited |
| Linear rational expectations solver | Klein-Sims QZ method for general linear RE systems; determines Blanchard-Kahn determinacy. | Full |
| DSGE simulation | Forward simulation of solved DSGE policy matrices under specified or random shock sequence. | Full |
| DSGE impulse response | Impulse response to single shock in solved DSGE model; orthogonal identification. | Full |
| DSGE variance decomposition | Forecast error variance share by shock at specified horizons in DSGE. | Full |
| DSGE calibration | Heuristic moment-matching calibration for RBC and NK; suggests parameters given targets. | Limited |
Statios supports canonical panel data estimators (FE, RE, Hausman test, 2SLS-IV), advanced dynamic panel methods (Arellano-Bond, System GMM), and modern causal inference designs (DiD, RDD, synthetic control, staggered DiD, IPW, mediation analysis).
| Procedure | What it does | Status |
|---|---|---|
| Fixed Effects Panel Regression | Within-transformation OLS eliminating time-invariant unobserved heterogeneity. Optional two-way (entity + time) fixed effects. | Full |
| Random Effects Panel Regression | GLS panel estimator assuming random effects uncorrelated with regressors. | Full |
| Hausman Specification Test | Tests FE vs RE using Moore-Penrose generalized inverse when covariance difference non-positive semidefinite (finite-sample robust). | Full |
| Two-Stage Least Squares (2SLS) IV | Instrumental variables regression with heteroskedasticity-robust first-stage F-test and weak-instrument diagnostics. | Full |
| Arellano-Bond Difference GMM | Dynamic panel estimator using lagged levels as instruments for differenced equations; includes Sargan and AR(1)/AR(2) tests. | Full |
| Blundell-Bond System GMM | Two-step efficient GMM stacking level and difference equations; Hansen J test and AR tests. | Full |
| Hausman-Taylor Estimator | Estimates time-varying exogenous, time-invariant exogenous, and endogenous coefficients using within and between equations. | Full |
| Between Estimator | OLS on entity-level means; removes within-group variation. | Full |
| First-Difference Estimator | Eliminates time-invariant heterogeneity via differencing; OLS on changes. | Full |
| Panel-Corrected Standard Errors (Beck-Katz PCSE) | Kernel-based covariance estimator robust to contemporaneous and lagged cross-sectional dependence. | Full |
| Driscoll-Kraay Standard Errors | Robust to both cross-sectional and temporal dependence via time clustering. | Full |
| Panel Cointegration Tests | Pedroni, Kao, or Fisher method tests for long-run equilibrium relationships in panel data. | Full |
| Panel Granger Causality (Fisher Combined) | Granger test per entity combined via Fisher's method across units. | Full |
| Cross-Sectional Dependence Test | Pesaran CD, Frees, or Friedman test for contemporaneous cross-unit correlation. | Full |
| Panel ARDL (Mean Group / Dynamic Fixed Effects) | Panel autoregressive distributed lag models: Mean Group (averages unrestricted per-entity long-run estimates) and Dynamic Fixed Effects (pooled with entity intercepts). Reports short-run, long-run, and ECM terms. | Full |
| Mundlak (Correlated Random Effects) | RE model augmented with entity means; joint test on means equivalent to Hausman test; robust to cluster heteroskedasticity. | Full |
| Panel RE Test (Breusch-Pagan LM) | Breusch-Pagan Lagrange-multiplier test for entity random effects in a panel: from pooled-OLS residuals it forms the two-sided BP-LM (chi-squared, 1 df) and the more powerful one-sided Honda statistic (normal) from grouped within-entity residual sums, handles balanced and unbalanced panels, and verdicts whether pooled OLS is adequate or a random/fixed-effects estimator is required. | Full |
| Panel Serial-Correlation Test (Wooldridge) | Wooldridge (2002) F-test for first-order serial correlation in the idiosyncratic errors of a linear panel model -- first-differences the equation, regresses the differenced residuals on their own lag and tests whether the slope equals the no-serial-correlation value of -0.5 (cluster-robust, as Stata xtserial), and also reports the Baltagi-Wu LBI. | Full |
| Anderson-Hsiao Dynamic Panel | Anderson-Hsiao IV estimator for dynamic panels: first-differences out the fixed effect, then instruments the differenced lagged dependent with its t-2 level via 2SLS, recovering a consistent persistence coefficient where pooled OLS and within are biased. | Full |
| FOD-GMM Dynamic Panel (Helmert) | Arellano-Bover (1995) forward-orthogonal-deviations (Helmert) GMM for dynamic panels: removes fixed effects via the FOD transform (which keeps the idiosyncratic errors serially uncorrelated, so the one-step weight is simply (Z'Z)^-1), instruments the transformed lagged dependent with the expanding set of lagged levels, and reports coefficient estimates (coef/se/z/p) with entity-clustered robust SEs and a Hansen J over-identification test - markedly less biased than the within estimator for short T. | Full |
| Bias-Corrected FE (dynamic panel) | Within (LSDV) fixed-effects estimation of a dynamic panel with a lagged dependent variable, then the analytical Nickell (1981) / Kiviet (1995) correction of the finite-T within (Nickell) bias subtracted from the autoregressive coefficient, returning the uncorrected and bias-corrected persistence plus the bias estimate. | Full |
| Parks-Kmenta FGLS (long panel) | Feasible GLS for long balanced panels (T > N) that simultaneously corrects for panel heteroskedasticity, contemporaneous cross-sectional correlation, and panel-specific AR(1) serial correlation, returning efficient slope estimates, per-entity AR(1) coefficients, and Parks-Kmenta standard errors (with the standard over-optimism caveat). | Full |
| Common Correlated Effects (CCE) | Pesaran (2006) CCE estimator for heterogeneous panels with cross-sectional dependence: augments each unit's regression with cross-sectional averages of the dependent and regressors to proxy and absorb the unobserved common factors, then reports the Mean-Group (CCEMG) or Pooled (CCEP) slopes, which remain consistent where plain fixed effects is biased by the factor. | Full |
| Panel Quantile Regression (Canay) | Estimates fixed-effects panel quantile regression via Canay's (2011) two-step estimator -- removing each entity's location-shift fixed effect from a within mean regression, then running ordinary quantile regression at tau on the de-fixed data -- yielding slope effects on the conditional tau-quantile that are robust to entity fixed effects (unlike biased pooled quantile regression). | Full |
| Error Components + AR(1) (Baltagi-Li) | One-way random-effects panel whose idiosyncratic error follows an AR(1) process: estimates the autocorrelation parameter rho, applies the Baltagi-Li Prais-Winsten transform and Sherman-Morrison FGLS, reports the variance components, the FGLS coefficient table, and a test of rho=0 (no serial correlation). | Full |
| Pooled Mean Group (PMG) | Estimates a dynamic heterogeneous panel ARDL error-correction model with common (pooled) long-run coefficients and unit-specific short-run dynamics and adjustment speeds via the Pesaran-Shin-Smith concentrated-likelihood iteration, reporting pooled long-run coef/se/z/p, the average error-correction speed, per-unit speeds, and a Hausman MG-vs-PMG poolability test. | Full |
| MINQUE/MIVQUE Variance Components | Estimates the between-entity and idiosyncratic variance components of a one-way error-components panel by Rao's non-iterative Minimum-Norm/Minimum-Variance Quadratic Unbiased estimator (handles unbalanced panels cleanly, unlike ANOVA), reports the implied intraclass correlation, and forms the FGLS slope estimates. | Full |
| Random-Trend Panel | Fixed-effects panel model that gives every entity its own intercept AND its own linear time trend (swept out via a double-within detrending transform), recovering the common predictor slopes purged of the unit-specific trends that would bias a plain one-way FE estimator. | Full |
| BLUP of Random Effects | Predicts each entity's unobserved random effect after a one-way random-effects (GLS) panel fit by shrinking its mean composite residual toward zero with the Henderson BLUP factor theta_i = sigma_u^2/(sigma_u^2 + sigma_e^2/T_i), reporting per-entity BLUPs, the shrinkage factor, and the variance components. | Full |
| Two-Way Random Effects | Two-way error-components GLS panel model with random entity AND time effects, estimating the entity, time, and idiosyncratic variance components (Wallace-Hussain) and reporting the Fuller-Battese theta transforms and FGLS coefficients. | Full |
| Panel Unit Root (IPS / LLC) | First-generation panel unit-root tests on a long panel: the Im-Pesaran-Shin W-bar statistic (standardised average of per-entity augmented Dickey-Fuller t-stats, allowing heterogeneous AR roots) plus the Levin-Lin-Chu pooled t* (common-rho, bias-adjusted), each returned with its N(0,1) p-value, the per-entity ADF t-stats and a stationary/unit-root verdict. | Full |
| Panel Cross-Sectional Dependence (BP-LM) | Tests panel errors for cross-sectional dependence from within-model residuals: the Breusch-Pagan LM (sum of T-weighted squared pairwise residual correlations ~ chi2(N(N-1)/2)), the Pesaran (2004) scaled LM, the Pesaran-Ullah-Yamagata (2008) bias-corrected scaled LM, and the reference Pesaran CD statistic, each with p-values and a verdict. | Full |
| CIPS Panel Unit Root (2nd-gen) | Pesaran (2007) cross-sectionally augmented IPS panel unit-root test, robust to cross-sectional dependence from a common factor: it averages per-entity CADF t-stats (each ADF regression augmented with the cross-section means of the lagged level and the differences) and compares the CIPS statistic against the non-standard Pesaran critical values. | Full |
| Panel Poolability (Chow-F) | Chow-type F test of panel poolability comparing restricted pooled-OLS SSR against the sum of unrestricted per-entity OLS SSRs to assess whether intercepts and slopes are homogeneous across entities, F~F((G-1)(k+1), N-G(k+1)). | Full |
| Difference-in-Differences (DiD) | 2x2 DiD with cluster-robust or HC1 SEs; includes group means and specification. | Full |
| Regression Discontinuity (Sharp RDD) | Local polynomial with triangular or uniform kernel; ad-hoc 1.5*SD bandwidth (not MSE-optimal) or user-specified. | Limited |
| Propensity Score Matching (PSM) | Logistic propensity score with 1-NN matching on logit scale; caliper on logit scale (Austin 2011); naive paired SE (Abadie-Imbens not applied). | Limited |
| Chow Structural Break Test | Tests equality of regression coefficients across two subsamples; F-statistic with df protection. | Full |
| Synthetic Control Method | Weighted combination of donors minimizing pre-treatment MSE; RMSPE ratio and period-level treatment effects. | Full |
| Event-study (dynamic two-way fixed effects) | Conventional dynamic TWFE event study: two-way FE with relative-time dummies; pre-treatment trend test; reference period omitted. | Full |
| Staggered DiD (Callaway & Sant'Anna 2021) | Group-time ATT estimates with not-yet-treated comparison group; bootstrap SEs; dynamic, group, and calendar aggregations. | Full |
| IV Diagnostics | First-stage F-tests, Cragg-Donald Wald F, Sargan over-identification, Durbin endogeneity test. | Full |
| Inverse-Probability Weighting (IPW) | Logistic propensity score weighting; ATE, ATT, or ATU estimands. | Full |
| Doubly Robust Estimation | Outcome model + propensity weighting; consistent if either model correct. | Full |
| Local Average Treatment Effect (LATE) | IV-based LATE for compliers only; asymptotic SE under 2SLS. | Full |
| Classical Mediation Analysis | Classical Baron-Kenny path decomposition (direct/indirect effects) with a Sobel test and a percentile bootstrap CI for the indirect effect. | Full |
| Sensitivity Analysis | E-value or Rosenbaum bounds for unobserved confounding robustness. | Full |
| Lee Bounds | Nonparametric bounds under sample selection on unobservables. | Full |
| Manski Treatment Bounds | Worst-case partial-identification (Manski 1990) bounds on the average treatment effect of a binary treatment, with monotone-treatment-response and monotone-treatment-selection refinements that tighten the identified ATE interval. | Full |
| Sharp Regression Kink Design (RKD) | Sharp RKD: estimates the change in the outcome's slope at the threshold via piecewise-linear local regression (assumes a known unit kink in the assignment-to-policy map). | Full |
| Bunching Estimator (normalized excess mass) | Estimates excess mass at a policy threshold by fitting a counterfactual polynomial density excluding the bunching region; reports the normalized excess mass (bunching ratio). | Full |
Covers exploratory and confirmatory dimensionality reduction, clustering, reliability, latent structure discovery, and item response modeling — from classical PCA/factor analysis through modern IRT, SEM, and categorical methods.
| Procedure | What it does | Status |
|---|---|---|
| Principal Component Analysis | Eigenvalue-based dimensionality reduction via correlation-matrix SVD with Kaiser criterion for retention. | Full |
| Exploratory Factor Analysis | ML factor extraction with Varimax/Promax rotation, KMO and Bartlett's sphericity test. | Full |
| K-Means Clustering | Standardized iterative partition with silhouette score; sample-based for n > 10k. | Full |
| Hierarchical Agglomerative Clustering | Distance-based clustering with Ward/complete/average linkage; limited to n ≤ 20k (O(n²) memory). | Limited |
| Cronbach's Alpha | Internal consistency reliability via item correlations with item-total and alpha-if-deleted. | Full |
| McDonald's Omega (Total) | ML single-factor CFA reliability coefficient without tau-equivalence assumption; requires k ≥ 3 items. | Full |
| Split-Half Reliability | Odd-even split with Spearman-Brown and Guttman corrections. | Full |
| Parallel Analysis | Horn's method: compares observed eigenvalues against 95th percentile of random-data eigenvalues. | Full |
| Multivariate Analysis of Variance | One-way MANOVA with Wilks' lambda, Pillai trace, Hotelling-Lawley, Roy's root and F-approximations. | Full |
| Linear Discriminant Analysis | Fisher discriminant functions and linear classification functions with resubstitution accuracy. | Full |
| Canonical Correlation Analysis | Maximum correlation between two variable sets via CCA with Rao's F-approximation for Wilks' lambda. | Full |
| Confirmatory Factor Analysis | Measurement model via semopy with factor loadings, residuals, and model fit (CFI, TLI, RMSEA, SRMR). | Full |
| Structural Equation Modeling | Full latent-variable SEM (measurement + structural paths) via semopy with standardized estimates. | Full |
| Path Analysis | Single-level directional relationships with path coefficients, R² per endogenous, and overall fit. | Full |
| Rasch Model (1PL IRT) | Joint MLE for dichotomous items; person abilities, item difficulties, infit/outfit, reliability. | Full |
| 2PL IRT Model | Two-parameter logistic with discrimination + difficulty; uses girth if available, else manual L-BFGS. | Full |
| 3PL IRT Model | Three-parameter logistic (discrimination, difficulty, guessing) via girth or manual EM. | Full |
| Graded Response Model (Samejima) | Ordinal polytomous items with cumulative thresholds per item; category response curves and item info. | Full |
| Partial Credit Model (Masters) | Polytomous step parameters without item discrimination; flexible by-item category counts. | Full |
| Rating Scale Model (Andrich) | Polytomous with common thresholds across items and item-specific location parameters. | Full |
| Nominal Response Model (Bock) | Unordered categorical responses with per-category slopes and intercepts per item. | Full |
| Differential Item Functioning Analysis | Mantel-Haenszel, logistic, or Lord's chi-squared DIF with ETS delta classification. | Full |
| Classical Test Theory Analysis | Item difficulty, corrected item-total correlation, and alpha-if-deleted per item. | Full |
| IRT Fit Indices (Approximate) | Limited-information chi-square, approximate RMSEA/CFI, Q3 local independence (NOT M2-based). | Limited |
| Multidimensional Scaling | Metric or non-metric embedding via Euclidean distances; stress and Shepard diagram. | Full |
| Simple Correspondence Analysis | SVD on contingency-table chi-square distances for two categorical variables. | Full |
| Multiple Correspondence Analysis | Indicator-matrix CA for k ≥ 2 categorical variables; uses prince or manual SVD. | Full |
| Gaussian Finite Mixture Model | k-component Gaussian mixture with BIC/AIC selection, posteriors, and component weights. | Full |
| Latent Class Analysis | EM-estimated discrete latent classes for binary/categorical indicators; entropy and model fit. | Full |
| Latent Profile Analysis | Diagonal-covariance GMM for continuous indicators with entropy, classification table. | Full |
| Categorical Regression (CATREG) | ALS optimal scaling for nominal/ordinal predictors with regression fit on quantified variables. | Full |
| Categorical PCA (CATPCA) | Optimal scaling PCA with ALS alternation; preserves ordinal/nominal structure. | Full |
| OVERALS (Non-linear CCA) | Multi-set non-linear canonical correlation via ALS with set-wise quantifications. | Full |
| Optimal Scaling Transform | Applies nominal/ordinal/spline quantifications to variables without modeling. | Full |
| Categorical Correlation | Pearson correlations on optimally-scaled categorical variables. | Full |
Statistical methods for survival analysis, Bayesian inference, meta-analytic pooling, and resampling-based inference. Includes Kaplan-Meier curves, Cox proportional-hazards regression, parametric and competing-risks models, Bayesian linear/logistic/hierarchical regression with MCMC diagnostics, fixed- and random-effects meta-analysis, and bootstrap/jackknife/permutation methods.
| Procedure | What it does | Status |
|---|---|---|
| Kaplan-Meier Survival Curves | Non-parametric survival estimation with Greenwood pointwise 95% CI and log-rank/multivariate log-rank test. | Full |
| Cox Proportional Hazards | Semi-parametric hazard regression with Efron tie handling, PH assumption test (Grambsch-Therneau), and concordance index. | Full |
| Mixture Cure Model | Fits a Berkson-Gage mixture cure model by EM, separating a logistic incidence submodel for the long-term-survivor (cured) fraction from a Weibull AFT latency submodel for the susceptible subjects who can still experience the event. | Full |
| Piecewise-Exponential Survival | Proportional-hazards survival model with a piecewise-constant baseline hazard, fitted by the Poisson / piecewise-exponential equivalence on person-interval exposure with covariate log-hazard-ratios; reduces to the exponential model with one interval. | Full |
| Discrete-Time Hazard (cloglog) | Fits a discrete-time (grouped-duration) proportional-hazards model by expanding subjects into person-period rows and estimating a complementary-log-log binomial GLM, yielding constant-across-time log-hazard-ratios that mirror the Cox model at low per-period hazard, plus a nonparametric or polynomial baseline-hazard summary. | Full |
| Actuarial Life Table | Cutler-Ederer life table with conditional survival/death probabilities, cumulative hazard rate, and Greenwood SE. | Full |
| Parametric Survival Models | Univariate or AFT parametric models (Weibull, exponential, lognormal, log-logistic, Gompertz) with parameter estimates. | Full |
| Accelerated Failure Time | AFT regression with covariates, time ratio estimates, concordance index, and AIC. | Full |
| Competing Risks (Fine-Gray) | Subdistribution-hazards model with cumulative incidence functions per event type and subdistribution HR. | Full |
| Shared Frailty Cox Model | Cluster-robust Cox with gamma frailty variance estimate and LR test vs no-frailty baseline. | Limited |
| Schoenfeld Residuals Test | PH assumption test per covariate and globally using scaled Schoenfeld residuals with interpretation. | Full |
| Nelson-Aalen Cumulative Hazard | Non-parametric cumulative hazard estimator with 95% pointwise confidence bands, optionally grouped. | Full |
| Stratified Log-Rank Test | Log-rank test with optional stratification, multivariate for 3+ groups, and pairwise comparisons. | Full |
| Restricted Mean Survival Time | RMST up to user-specified tau with SE and 95% CI; difference test and CI for 2 groups. | Full |
| Time-Varying Cox Model | Cox regression with time-varying covariates (start-stop format), HR with CI and partial log-likelihood. | Full |
| Cox Residuals for Diagnostics | Five residual types (martingale, deviance, Schoenfeld, scaled Schoenfeld, score) for model diagnostics. | Full |
| Bayesian Linear Regression | MCMC-based (PyMC or emcee fallback) with weakly informative priors, posterior samples, PPC, and diagnostics (R-hat, ESS). | Full |
| Bayesian Unit-Root Analysis | Closed-form Student-t posterior for the AR(1) root rho under a flat/Jeffreys prior, reporting the posterior mean/sd, a 95% credible interval, and the posterior probabilities P(rho>=1) (unit root/explosive) and P(rho<1) (stationary). | Full |
| Bayesian VAR (Minnesota prior) | Estimates a reduced-form VAR(p) with the Minnesota (Litterman) shrinkage prior imposed by dummy observations, reporting posterior-mean coefficient matrices that center the own first lag at 1 (levels) or 0 (stationary), shrink cross-variable lags harder than own lags by 1/lag, plus the shrinkage hyperparameters, per-equation in-sample fit, and a multi-step forecast. | Full |
| Bayesian VECM (cointegration) | Bayesian Vector Error Correction Model for cointegrated series: given a chosen cointegrating rank and lag order, places a weakly-informative Normal-inverse-Wishart prior on the short-run dynamics and adjustment coefficients and Gibbs-samples the posterior, reporting posterior means and 95% credible intervals for the speed-of-adjustment loadings (alpha), the normalised cointegrating vectors (beta), the short-run matrices, and error-correction evidence. | Full |
| Bayesian CFA (one-factor) | Fits a one-factor confirmatory measurement model x_ij = lambda_j * eta_i + eps_ij by a three-block Gibbs sampler (factor scores, loadings, residual variances), returning posterior loading summaries with 95% credible intervals, residual (uniqueness) variances, implied communalities, and an SRMR-based fit summary. | Full |
| Bayesian IRT (2PL) | Bayesian two-parameter logistic item-response model fitted by a Metropolis-within-Gibbs MCMC sampler, returning posterior item discriminations and difficulties (with 95% credible intervals) plus latent person-ability summaries on the N(0,1)-identified scale. | Full |
| Bayesian Meta-Analysis | Fits a random-effects meta-analysis (theta_i ~ N(mu, tau^2), y_i ~ N(theta_i, se_i^2)) by a direct Gibbs sampler with a weakly-informative half-Cauchy prior on the between-study sd, returning the posterior overall effect mu with credible interval, between-study heterogeneity tau, shrunken per-study estimates, and the posterior probability the effect is positive. | Full |
| Conjugate Bayesian Regression (Normal-Gamma) | Analytic conjugate Bayesian linear regression with a Normal-Gamma (Normal-inverse-Gamma) prior that returns closed-form multivariate-t posteriors for the coefficients and an inverse-Gamma posterior for the error variance, reducing exactly to OLS under a flat prior and shrinking slopes toward zero under a tight prior, with no MCMC. | Full |
| Bayesian Quantile Regression | Estimates the conditional tau-quantile of an outcome via the asymmetric-Laplace likelihood, sampling the posterior of the quantile-regression coefficients with the Kozumi-Kobayashi (2011) location-scale-mixture Gibbs sampler and reporting posterior means, sds and 95% credible intervals. | Full |
| Student-t (robust) Regression | Outlier-robust linear regression with iid Student-t(nu) errors fitted by an EM / IRLS scale-mixture ML algorithm, downweighting extreme residuals so the fit tracks the bulk of the data rather than the outliers, and reporting coefficients/SEs, the error scale, the (optionally ML-estimated) degrees of freedom, log-likelihood and per-observation robustness weights. | Full |
| Metropolis-Hastings (Bayesian regression) | Random-walk Metropolis-Hastings MCMC sampler for the joint posterior of regression coefficients and the error variance (beta, log sigma^2) in Bayesian linear regression, with a proposal auto-tuned toward the optimal ~0.234 acceptance rate, reporting posterior means/sds/95% credible intervals, the acceptance rate, and effective sample sizes. | Full |
| Gibbs Sampler (Bayesian regression) | Conjugate Gibbs sampler for Bayesian linear regression that alternates Normal draws of the coefficients given sigma^2 and inverse-Gamma draws of sigma^2 given the coefficients, returning posterior means/sds/95% credible intervals, effective sample size and a Geweke convergence diagnostic (acceptance is identically 1), with weak-prior posteriors that coincide with OLS. | Full |
| Laplace Approximation (Bayesian) | Approximates the posterior of a Bayesian linear or logistic regression by a Gaussian centered at the posterior mode (MAP) with inverse-Hessian covariance, reporting approximate posterior means/SDs/credible intervals, the Laplace approximation to the log marginal likelihood (model evidence), and a Tierney-Kadane fully-exponential refined posterior mean. | Full |
| Importance Sampling (Bayesian) | Bayesian posterior inference for a linear regression by importance sampling / sampling-importance-resampling: proposals drawn from a heavy-tailed multivariate-t centred at OLS (covariance inflated) under a weakly-informative conjugate Normal-inverse-Gamma prior are reweighted to give importance-weighted posterior means, 95% credible intervals for the coefficients and error variance, the weights' effective sample size, and a resampled (SIR) posterior draw. | Full |
| Bayesian Poisson Regression | Fits a log-linear Poisson count model by random-walk Metropolis-Hastings under a weak Normal prior, returning posterior means, standard deviations and 95% credible intervals for each coefficient, the incidence-rate ratios exp(coef), and the Metropolis acceptance rate (tuned toward the ~0.234 optimal scaling). | Full |
| Bayesian Tobit | Left-censored (Tobit) regression estimated by Gibbs sampling with data augmentation of the censored latent responses, returning posterior means and 95% credible intervals for the coefficients and error standard deviation plus the censored fraction. | Full |
| Bayesian Probit (Albert-Chib) | Fits a binary probit model by Gibbs sampling with Albert-Chib truncated-normal latent-utility data augmentation under a diffuse Normal prior, returning posterior means, SDs, 95% credible intervals, effective sample sizes, and the frequentist MLE as a reference. | Full |
| Horseshoe Regression (sparse) | Bayesian linear regression with a horseshoe shrinkage prior (Carvalho-Polson-Scott 2010) sampled by the fully-conjugate Makalic-Schmidt (2016) inverse-gamma scale-mixture Gibbs sampler, reporting posterior means and 95% credible intervals per coefficient, per-coefficient shrinkage weights kappa = 1/(1 + n*tau^2*lambda^2), and which coefficients are effectively selected (CI excludes 0) so that on sparse designs the null coefficients are shrunk hard toward 0 while genuine signals stay near their true values. | Full |
| Spike-and-Slab Variable Selection | Bayesian stochastic search variable selection (George-McCulloch SSVS) that places a two-component spike-at-zero / diffuse-slab prior on every regression coefficient and Gibbs-samples Bernoulli inclusion indicators to report each predictor's posterior inclusion probability (PIP), Bayesian-model-averaged coefficients, and the most probable visited models. | Full |
| Bayesian Lasso | Bayesian lasso regression (Park & Casella) places a Laplace prior on the coefficients and samples it with a scale-mixture-of-normals Gibbs sampler, returning shrunk posterior-mean coefficients with 95% credible intervals, the posterior of the shrinkage parameter lambda, and which predictors are effectively selected (CI excludes 0). | Full |
| Adaptive Lasso | Weighted lasso (Zou 2006) whose per-coefficient penalties come from an initial OLS/ridge estimate, delivering oracle variable selection: truly-zero coefficients are driven to exactly 0 while genuine signals stay nearly unbiased, with the penalty chosen by reproducible cross-validation. | Full |
| Savage-Dickey Bayes Factor | Computes the Savage-Dickey density-ratio Bayes factor (BF01/BF10) for the sharp null H0: beta=0 on one regression coefficient by taking the ratio of the marginal posterior to the prior density at zero under a conjugate Normal-prior Bayesian regression, returning posterior mean/sd, the prior sd, and a Jeffreys-scale verdict. | Full |
| Bayesian IC (DIC/WAIC/LOO) | Bayesian predictive information criteria for a conjugate-Gibbs Bayesian linear regression, reporting DIC (Spiegelhalter), WAIC (Watanabe) and Pareto-smoothed importance-sampling LOO (Vehtari-Gelman-Gabry) on the deviance scale with their effective-parameter counts (p_DIC, p_WAIC, p_LOO) and Pareto-k diagnostics, for principled out-of-sample model comparison. | Full |
| Bayesian Model Averaging | Enumerates all 2^k linear-regression covariate subsets, scores each by BIC to obtain approximate posterior model probabilities, and reports per-predictor posterior inclusion probabilities (PIP), BMA posterior means/sds of the coefficients (averaged over models), and the top models. | Full |
| Bayesian Logistic Regression | Bernoulli likelihood with logit link, posterior odds ratios, in-sample accuracy, and MCMC diagnostics. | Full |
| Bayesian Hierarchical Model | Random-intercept multilevel model (PyMC, emcee, or OLS shrinkage fallback) with ICC, varying intercepts per group. | Limited |
| MCMC Diagnostics | Gelman-Rubin R-hat, ESS via autocorrelation, trace summaries with interpretations of convergence. | Full |
| Bayes Factor | Model comparison via BIC approximation with Jeffreys-scale interpretation (decisive/strong/moderate/anecdotal). | Limited |
| Prior vs Posterior | Visualization comparison of prior and posterior distributions per parameter. | Full |
| Posterior Predictive Check | Bayesian p-value and visual comparison of observed vs simulated data from posterior predictive. | Full |
| Credible Interval | Highest density interval (HDI) and equal-tailed interval (ETI) at 94% credibility. | Full |
| Fixed-Effects Meta-Analysis | Inverse-variance weighting, Cochran Q, I², per-study and pooled CIs, assumes homogeneous effects. | Full |
| Random-Effects Meta-Analysis | DerSimonian-Laird tau² estimation, I², 95% prediction interval (Higgins-Thompson-Spiegelhalter), per-group weights. | Full |
| Egger's Test for Publication Bias | Funnel-plot asymmetry test via standardized-effect intercept regression, t-test on k-2 df. | Full |
| Forest Plot Data | Per-study effects with 95% CI, pooled estimate (fixed or random), I², and heterogeneity for visualization. | Full |
| Bootstrap Standard Error | Bootstrap distribution of a statistic (mean/median/std/var/quantiles) with SE, bias, and summary. | Full |
| Wild Bootstrap (OLS) | Wild (and wild-cluster) bootstrap standard errors, percentile CIs, and p-values for OLS coefficients, robust to heteroskedasticity and the standard tool for few-cluster inference. | Full |
| Percentile-t Bootstrap (coefficient) | Studentized (bootstrap-t) confidence interval for a single OLS regression coefficient, built by pairs-resampling the pivot t*=(b*-b)/se* and inverting it via the Hall pivot-flip formula for second-order-accurate, skewness-corrected coverage, contrasted against the normal-theory b +/- 1.96 se interval. | Full |
| Block Bootstrap (time series) | Moving-block and circular-block bootstrap that resamples overlapping blocks of consecutive observations to deliver dependence-robust standard errors and percentile confidence intervals for the mean, median, variance, or lag-1 autocorrelation of a time series. | Full |
| Parametric Bootstrap (OLS) | Resamples OLS errors from the fitted homoskedastic-Gaussian model (y* = X*beta_hat + N(0, sigma^2)), refits each synthetic sample, and reports per-coefficient bootstrap SEs and percentile 95% CIs that converge to the classical OLS standard errors as replications grow. | Full |
| Residual Bootstrap (OLS) | Model-based (residual) bootstrap that holds the design and fitted values fixed, resamples OLS residuals with replacement to build synthetic responses, refits each, and reports per-coefficient bootstrap standard errors and percentile 95% confidence intervals without assuming any error distribution. | Full |
| Subsampling Inference | Politis-Romano-Wolf subsampling: draws subsamples of size b < n without replacement, recomputes a statistic on each, and uses the sqrt(b)-rescaled subsampling distribution to build a confidence interval and standard error that are stable as b varies. | Full |
| Bayesian Bootstrap (Rubin) | Rubin's Bayesian bootstrap for OLS coefficients: draws Dirichlet(1,...,1) weights over the observations and refits weighted least squares each replication to produce a posterior distribution (mean, sd, 95% credible interval) for every coefficient, first-order equivalent to the ordinary bootstrap. | Full |
| BCa Bootstrap Interval | Bias-corrected and accelerated (BCa) bootstrap 95% confidence interval for a chosen scalar statistic (mean, median, std, variance, quartiles, IQR) of one numeric variable, correcting the plain percentile interval for median bias (z0) and skewness (jackknife acceleration a). | Full |
| Cluster Bootstrap (OLS) | Pairs cluster bootstrap for OLS coefficient inference that resamples whole clusters with replacement and refits to produce cluster-robust bootstrap standard errors and percentile confidence intervals valid under arbitrary within-cluster error correlation. | Full |
| Bootstrap Confidence Interval | Four CI methods (percentile, basic, BCa, studentized) with distribution summary and downsampled samples. | Full |
| Bootstrap Regression | Row-level resampling with OLS per iteration; bootstrap SE, bias, CI, and comparison to classical estimates. | Full |
| Jackknife Estimation | Leave-one-out resampling, SE, bias correction, and bias-corrected estimate. | Full |
| Permutation Test (Two-Sample) | Exact resampling test for mean/median/t-stat difference with null distribution and two-tailed p-value. | Full |
| Permutation Test (Correlation) | Exact test for Pearson/Spearman/Kendall correlation with null distribution and significance. | Full |
| Cross-Validation | K-fold CV (sklearn-compatible) for linear/logistic/ridge/lasso with multiple scoring metrics. | Full |
| Bayesian Bootstrap | Dirichlet-weighted resampling for posterior distribution of a statistic; Bayesian alternative to classical bootstrap. | Full |
Statios provides machine learning and choice modeling tools: supervised learning with explainability (Random Forest, Gradient Boosting, SVM, Decision Trees, neural networks); model evaluation (cross-validation, ROC/AUC, confusion matrix, feature importance, SHAP); and conjoint analysis (traditional, choice-based, hierarchical Bayes) for preference modeling.
| Procedure | What it does | Status |
|---|---|---|
| Random Forest | Fit random forest for classification or regression with feature importances and OOB score. | Full |
| Gradient Boosting | Fit gradient boosted model (XGBoost if available) with learning curves and feature importances. | Full |
| Support Vector Machine | Fit SVM for classification or regression with standardized features and support vector counts. | Full |
| Decision Tree | Fit decision tree with tree rules, feature importances, and depth/leaf metrics. | Full |
| Cross-Validation | K-fold cross-validation with train/test scores and timing metrics across folds. | Full |
| ROC/AUC Analysis | Binary classification ROC curve with AUC and Youden's J optimal threshold. | Full |
| Confusion Matrix | Multi-class confusion matrix with per-class precision/recall/F1, normalizations, and macro/weighted averages. | Full |
| Classification Metrics | Accuracy, precision, recall, F1, Matthews correlation, Cohen's kappa, and log-loss. | Full |
| Feature Importance | Built-in and permutation-based feature importances with standard errors. | Full |
| SHAP Values | SHAP values via TreeExplainer (exact TreeSHAP) or KernelExplainer (model-agnostic) with expected values. | Limited |
| SHAP Summary Plot | SHAP summary (beeswarm) plot data with feature importance ranking and per-sample contributions. | Full |
| Train/Test Predict | Fit model on train set and return test predictions, actuals, residuals/probabilities. | Full |
| MLP Classifier | Scikit-learn multilayer perceptron for classification with configurable hidden layers and activation. | Full |
| MLP Regressor | Scikit-learn multilayer perceptron for regression with configurable hidden layers and activation. | Full |
| Deep Neural Network | Keras/TensorFlow deep network for classification or regression with custom architecture, epochs, batch size. | Full |
| Autoencoder | Unsupervised autoencoder for dimensionality reduction and feature learning. | Full |
| CNN for Tabular Data | Convolutional neural network adapted for tabular classification or regression. | Full |
| Neural Network Hyperparameter Tuning | Grid search over neural network hyperparameters with cross-validation. | Full |
| CHAID | Chi-squared automatic interaction detection for tree-based classification. | Limited |
| QUEST | Quick, unbiased, efficient statistical tree method for classification. | Limited |
| C5.0 | C5.0 decision tree and rule learner for classification. | Limited |
| Ensemble Trees Comparison | Side-by-side comparison of multiple tree-based ensemble methods. | Limited |
| Tree Export | Export trained tree model in formats suitable for deployment. | Limited |
| Compare Trees | Comparative metrics across tree models (accuracy, depth, interpretability). | Limited |
| Traditional Conjoint Analysis | Rating-based conjoint via OLS with dummy-coded attributes and part-worths. | Full |
| Choice-Based Conjoint | Aggregate multinomial logit conjoint from choice data with part-worths and hit rate. | Full |
| Hierarchical Bayes CBC | Empirical-Bayes iterated-MAP CBC with individual part-worths (not true HB-MCMC). | Limited |
| Market Simulator | Market share simulation given part-worths and scenarios (logit, first-choice, randomized). | Full |
| Attribute Importance | Relative attribute importance from part-worth ranges (% allocation). | Full |
| Conjoint Design | Fractional experimental design generation (full, orthogonal, D-optimal). | Full |
| Conjoint Segmentation | Segment respondents using K-means on individual part-worths. | Full |
Statios provides complex survey design analysis and missing data handling. Covers design-based inference (Taylor linearization, replicate weights, Rao-Scott corrections) and advanced imputation (MICE, EM, PMM) with proper uncertainty propagation.
| Procedure | What it does | Status |
|---|---|---|
| Survey design definition | Specify strata, clusters, sampling weights, and finite population correction; summarize design. | Full |
| Survey-adjusted means | Design-based estimates with Taylor linearization variance; includes DEFF per variable. | Full |
| Survey-adjusted totals | Weighted population totals with design-adjusted standard errors and DEFFs. | Full |
| Survey proportions | Design-based category proportions with confidence intervals and design effects. | Full |
| Survey-weighted regression | Linear or logistic regression via WLS with design-adjusted sandwich covariance. | Full |
| Design Effect (DEFF) | Ratio of design variance to SRS variance for a mean; includes interpretation guide. | Full |
| Replicate weights analysis | Jackknife, BRR, or bootstrap SE/CI from replicate weight methodology. | Full |
| Survey t-test | Two-sample design-adjusted t-test on difference of weighted means. | Full |
| Rao-Scott chi-square | Chi-square test for categorical association; first-order design effect correction. | Full |
| Missing value summary | Comprehensive report of missing counts, patterns, and complete-case counts per variable. | Full |
| Little's MCAR test | Test whether missingness is completely random; EM-based ML with chi-square inference. | Full |
| Simple imputation | Mean, median, or mode imputation for a single variable. | Full |
| KNN imputation | Impute missing via k nearest neighbors; standardizes features to avoid scale-dependence. | Full |
| MICE imputation | Multiple imputation by chained equations; pooled estimates via Rubin's rules. | Full |
| EM imputation | EM algorithm for missing data assuming multivariate normality; returns imputed dataset. | Full |
| Predictive Mean Matching (PMM) | Imputation via OLS prediction + nearest-neighbor donor draw; preserves observed distribution. | Full |
| Missing data patterns | Identify missing patterns; check monotone vs non-monotone structure; Little's MCAR suggestion. | Full |
| MNAR sensitivity analysis | Shift imputed values by delta range; track regression coefficient changes to find tipping point. | Full |
Data management, transforms and workflow procedures in Statios: dataset loading and multi-dataset workflows, direct cell editing with undo/redo, spreadsheet-style formulas for derived variables, variable metadata configuration (types, labels, measurement levels), data transformations (compute, recode, categorize, standardize, filter, lag/lead, dummy), data manipulation (merge, reshape, aggregate, collapse, append, sort, subset, deduplicate, rank, transpose, split/concat columns, pivot), data-to-table formatting, and session/macro recording for reproducible workflows.
| Procedure | What it does | Status |
|---|---|---|
| Load Dataset | Load data from file (CSV, TSV, XLSX, SPSS .sav, Stata .dta, JSON) into active workspace. | Full |
| Get Dataset Info | Retrieve metadata for active dataset: row/column counts, dtypes, missing cells, columnar status. | Full |
| Get Data Window | Fetch windowed rows (e.g., 100 rows at offset 0) for grid rendering. | Full |
| Get Full Columns | Retrieve whole columns for chart building; large datasets sampled with fixed seed for row alignment. | Full |
| Get Variables | Inferred variable metadata (type, measure, labels, value labels) merged with engine-owned overrides. | Full |
| List Datasets | List all open datasets and current active dataset ID. | Full |
| Switch Active Dataset | Set a dataset as active (default target for all analyses and edits). | Full |
| Close Dataset | Remove a dataset from memory; clears associated undo/redo stacks. | Full |
| Duplicate Dataset | Create independent copy of dataset with all metadata and current state. | Full |
| Rename Dataset | Change display name (in-memory; original file unchanged). | Full |
| Export Dataset | Write active dataset to file (CSV, XLSX, SAV, DTA, JSON) with variable metadata. | Full |
| Set Cell Value | Edit single cell; coerces value to column dtype; generates undo entry. | Full |
| Undo Cell Edit | Reverse last cell edit (stack cap 200 per dataset). | Full |
| Redo Cell Edit | Restore undone cell edit. | Full |
| Insert Row | Insert blank row at index; structural edit clears undo stacks. | Full |
| Delete Row | Remove row at index; structural edit clears undo stacks. | Full |
| Add Variable | Append new column (all-NaN or default name auto-generated). | Full |
| Delete Variable | Remove column; structural edit clears undo stacks. | Full |
| Sort Dataset | Sort by column(s) ascending/descending; structural edit clears undo stacks. | Full |
| Evaluate Formula | One-off scalar formula (e.g., =A1+B1); returns value or Excel-style error code. | Full |
| Apply Formula to Column | Fill formula down entire column as derived variable; stored for recomputation. | Full |
| Recompute Derived Columns | Recalculate all derived columns after data edits in dependency order; reports #REF! for missing inputs. | Full |
| Get Formulas | Retrieve stored formula metadata: source, refs (letter→column), anchor row, dependencies. | Full |
| Set Variable Type | Convert column dtype (numeric/string/date/boolean) with honest failure accounting. | Full |
| Set Measurement Level | Assign measure (nominal/ordinal/scale) for SPSS Variable View parity. | Full |
| Set Value Labels | Map raw values to display labels (e.g., 1→'Yes', 0→'No'); empty list clears labels. | Full |
| Auto-Encode Categorical | SPSS-style: text categories → numeric codes with inverse map as value labels. | Full |
| Compute Variable | Create new variable from AST-safe expression (log, sqrt, abs, sin, cos, mean, std, +−*/). Stores result in new column. | Full |
| Recode Variable | Map values by pattern (e.g., '1-30'→'low', '31+'→'high') into new column. | Full |
| Standardize Variable | Transform to z-scores (mean 0, SD 1), min-max [0,1], or robust (median, IQR). | Full |
| Categorize / Bin | Convert numeric to categories: equal-width, quantile-based, or custom breaks. | Full |
| Filter Cases (Read-Only) | Report which rows match a condition (e.g., age > 30); read-only—use data.subset to drop. | Full |
| Create Dummy Variables | Expand categorical to 0/1 indicators; optionally drop first to avoid collinearity. | Full |
| Lag / Lead | Shift values k periods forward/backward for time-series predictors. | Full |
| Merge Datasets | SQL-like join (inner/left/right/outer/cross) on key column(s); reports match/unmatch counts. | Full |
| Reshape Long | Convert wide → long: multiple value columns become one 'value' + one 'variable' column. | Full |
| Reshape Wide | Convert long → wide: spread key column values across columns. | Full |
| Aggregate Data | Group by column(s) and compute summaries (mean/sum/count/etc.); produces new dataset. | Full |
| Collapse Data | Similar to aggregate; summarize grouped data (default stats: mean, SD, N). | Full |
| Append Datasets | Vertically stack 2+ datasets (row-bind); common columns aligned. | Full |
| Sort Dataset | Sort by column(s) ascending/descending; optionally replace active or create new dataset. | Full |
| Subset / Filter | Keep rows matching condition (AST-safe); replace active dataset. | Full |
| Deduplicate | Remove duplicate rows (keep first/last/none); optionally subset columns first. | Full |
| Transpose Dataset | Flip rows ↔ columns; optionally use one column as new row index. | Full |
| Split Column | Split text column by separator into multiple new columns. | Full |
| Concatenate Columns | Join 2+ columns into one (e.g., first + last → fullname). | Full |
| Rank Variable | Convert to ranks (1,2,3...); methods: average (ties split), min/max, dense, ordinal. | Full |
| Crosstab Counts | Build contingency table (row × col) from two categorical variables; include marginals. | Full |
| Pivot Longer (Complex) | Advanced wide→long: regex on column names extracts multiple groups into separate columns. | Full |
| Save Session | Persist all datasets, variable metadata, and output history to .stios JSON. | Full |
| Load Session | Restore datasets, metadata, and outputs from .stios; repopulates DatasetManager. | Full |
| Session Info | Read session metadata (creation date, version, dataset names, row/col counts). | Full |
| Session Diff | Compare two .stios files; report differences in datasets and outputs. | Full |
| Start Macro Recording | Begin recording user actions (transforms, analyses) for later replay. | Full |
| Stop Macro Recording | End current macro recording session. | Full |
| Save Macro | Write recorded actions to .sfmacro JSON file. | Full |
| Load Macro | Read .sfmacro file; return actions ready to replay. | Full |
| Replay Macro | Execute recorded actions in sequence (transform.compute, data.*, etc.) on current dataset. | Full |
| Session Undo | Reverse a logged action by ID (currently stub; per-dataset cell undo used instead). | Limited |
| Session Redo | Restore undone action by ID (currently stub; per-dataset cell redo used instead). | Limited |
| Export Script | Convert recorded macro actions to Python or R script (reproducible workflow). | Full |
| Regression Table | Format regression results (multiple model specs) in APA/HTML/LaTeX table. | Full |
| Descriptives Table | Format descriptive statistics by groups in APA/HTML/LaTeX table. | Full |
| Correlation Table | Format correlation matrix in APA/HTML/LaTeX; supports Pearson, Spearman, Kendall. | Full |
| Crosstab Table | Format contingency table (observed, expected, residuals) in APA/HTML/LaTeX. | Full |
| Model Comparison Table | Side-by-side regression models with fit statistics and significance stars. | Full |
| ANOVA Table | Format one-way/repeated-measures ANOVA table in APA/HTML/LaTeX. | Full |
| Factor Loading Table | Format EFA/PCA loadings with rotation (varimax, promax, etc.). | Full |
AI-powered statistical guidance, automated analysis pipelines, publication-quality graphics and report generation, plus a visual rule-based workflow recommender (Guided Path) and a node-based pipeline editor (RLab).
| Procedure | What it does | Status |
|---|---|---|
| Statios AI assistant | Ask the built-in assistant in plain language; it chooses an analysis, runs it on the local engine, and interprets the result. Routed through the Statios relay — your raw data rows never leave your device. | Full |
| Summarize iteration | Summarizes executed analyses into key findings as structured JSON (used by the Research Path); routed through the Statios relay. | Full |
| Auto data quality check | Single-call data quality report: missing patterns, duplicates, type consistency, cardinality anomalies. | Full |
| Auto assumption validation | Detect violations of key statistical assumptions (normality, homogeneity, linearity, etc.) for a given test type. | Full |
| Auto exploratory data analysis | One-command EDA: univariate summaries, correlation matrix, key visualizations, and insight extraction. | Full |
| Compare candidate models | Fit multiple regression models (OLS, ridge, lasso, elastic net, robust) on same outcome; rank by IC/CV. | Full |
| Histogram | Distribution visualization for a numeric variable with Sturges' bin count and descriptive stats. | Full |
| Scatter plot | Bivariate X–Y plot with optional grouping and Pearson correlation in response. | Full |
| Box plot | Median, quartiles, and outliers per group (or ungrouped); includes min/max and sample size. | Full |
| Bar chart | Categorical value counts or aggregated numeric summaries, with optional value labels. | Full |
| Export statistical table | Write a result table to DOCX (APA style), LaTeX, HTML, Markdown, or CSV. | Full |
| Generate full report | Build a report document from analysis sections with optional citations; export as Markdown, HTML, or LaTeX. | Full |
| Build report document | Assemble a deterministic report model from journal entries, dataset metadata, and optional guided plan; handles APA 7 formatting and citation aggregation. | Full |
| Export report to file | Render and write a report model as HTML, Markdown, or BibTeX; optionally saves .bib sidecar. | Full |
| Recommend analysis path | Profile-based deterministic method selector: returns step plan with matched rule, alternatives, and aggregated bibliography. | Full |
| Generate executable plan DAG | Convert study profile + matched rule into a directed graph with nodes (analyses), edges (unconditional backbone + conditional branches), and bound parameters. | Full |
| Profile dataset | Introspect active dataset and return partial StudyProfile (data-determined fields only); leaves interview unknowns and variable candidates for UI. | Full |
| AI-guided interview turn | Constrained AI conversation that fills StudyProfile fields only via strict-JSON validation; forbids methodological advice. | Full |
| Generate methods section | Render a plan DAG as AI-authored academic methods prose (intro, phase paragraphs with inline APA 7 citations, alphabetical reference list, BibTeX export); each paragraph is citation-validated against the MKB registry with a template-based fallback. Replaces the earlier template-only justification. | Full |