AI, Automation, Graphics & Reporting
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).
19 procedures. Every result is computed by the open Python engine and is exportable to APA tables and reports.
| Procedure | What it does | Status | Key reference |
|---|---|---|---|
| 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 | Virtanen et al. (2020) |
| Auto assumption validation | Detect violations of key statistical assumptions (normality, homogeneity, linearity, etc.) for a given test type. | ✅ Full | Virtanen et al. (2020) |
| Auto exploratory data analysis | One-command EDA: univariate summaries, correlation matrix, key visualizations, and insight extraction. | ✅ Full | Virtanen et al. (2020) |
| Compare candidate models | Fit multiple regression models (OLS, ridge, lasso, elastic net, robust) on same outcome; rank by IC/CV. | ✅ Full | Virtanen et al. (2020) |
| 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 | — |