Statistical Methods Reference
Statios ships 600 statistical procedures across 11 domains, all computed by an open, auditable Python engine. This reference is the canonical list of what the software does today.
- 569 procedures are fully supported.
- 31 are available with a documented limitation (listed on each page and in Capabilities & Roadmap).
| 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). |
Many procedures report effect sizes, confidence intervals, robust standard errors and assumption diagnostics by default, and attach citations you can export with the result.