EnTraineR 1.0.0

First major release.
This version consolidates the API, improves robustness for FactoMineR outputs, and harmonizes audience-tailored prompts across all functions.

Major Changes & New Features

Unified Architecture (“Hub & Spoke”)

All trainer functions now rely on a centralized core (trainer_core.R) for consistent prompt building, audience profiling, output capture, and regex/quoting helpers. Any update to the core instantly benefits all trainers.

New Trainers

• trainer_t_test() — Interpret Student’s t-tests (one-sample, two-sample, paired, Welch) with audience-aware guidance on p-values vs confidence intervals.
  
• trainer_cor_test() — Interpret correlation tests (Pearson, Spearman, Kendall) with clear distinction between statistical significance and practical magnitude.
  
• trainer_var_test() — Interpret F-tests for equality of variances.

Robust ANOVA & Linear Model Support

• trainer_AovSum() and trainer_LinearModel() use centralized heuristics (trainer_core_extract_tables_heuristic) to reliably extract F-test and T-test tables even when standard headers are missing (common with capture.output).
  
• Strict, space-safe T-table filtering: factor names with spaces or special characters are handled via core quoting (trainer_core_quote) and detection helpers.

Gemini Integration

• gemini_generate() enables direct interaction with Google’s Gemini API.
  
• Includes automatic retry with exponential backoff and clear error surfacing (finish reasons, safety blocks).
  
• Adds compile_to = "html" and compile_to = "docx" to directly render LLM responses into reports.

Improvements

Pedagogy & Interpretability

• PCA/MCA: Prompts explicitly guide interpretation of correlation signs (PCA) and category positions (MCA) to prevent hallucinations.
  
• ANOVA: “How-to-read” blocks clarify deviation coding (sum-to-zero); the Intercept is the Grand Mean.
  
• t-test: Clear separation between Inference (p-value) and Magnitude (effect size/mean difference) in prompts.

Developer Experience

• Internal helpers for regex, capture, quoting centralized for DRY code.
  
• Improved error messages when input objects are NULL or of the wrong class.
  
• Audience sections standardized (BEGINNER/APPLIED/ADVANCED) with consistent limits (e.g., Takeaway ≤ 5 bullets).

Bug Fixes

• Fixed duplication of extract_section/header-loss issues by using the shared core heuristic and single re-attachment of T-table headers after filtering.
  
• Fixed potential regex failures with factor names containing spaces in trainer_AovSum.
  
• Removed unnecessary stringr dependency in examples.

EnTraineR 0.9.0

Major pre-CRAN update with bug fixes, improved prompts, and Gemini API support.

Highlights

• New: gemini_generate() — minimal, robust wrapper for Google Gemini (Generative Language API) to programmatically generate LLM responses from R.
  • Normalizes model ids (accepts "gemini-2.5-flash" or "models/gemini-2.5-flash").
  • Correct endpoint (v1beta/models/{model}:generateContent) and query key.
  • Config params: temperature, top_p, top_k, max_output_tokens, stop_sequences, system_instruction, seed, timeout, verbose.
  • Adds polite User-Agent header by default: EnTraineR/0.9.0 (https://github.com/Sebastien-Le/EnTraineR).
  • Safer parsing: handles empty/blocked candidates and reports finishReason or safety blocks with clear errors.

Bug fixes & robustness

• Fixed occasional header-loss in filtered ANOVA T-test tables; always re-attaches the column header exactly once when filtering.
• Resolved errors in trainer_LinearModel() when global-fit lines were partially printed (missing df or p). Now defensive against missing fields.
• Stabilized audience sections across trainers (BEGINNER/APPLIED/ADVANCED) with consistent wording and limits (e.g., “Takeaway ≤ 5 bullets”).
• Cleaned up duplicate attachment issues in verbatim blocks.
• Improved error messages when external APIs return 404 or safety-filtered results.

Documentation & examples

• Expanded examples for:
  • trainer_LinearModel() (ham and deforestation case studies).
  • trainer_AovSum() (sensory chocolates and poussin datasets).
• Added dataset docs for deforestation, ham, poussin with guarded examples for optional dependencies.
• DESCRIPTION improved (clear package blurb) and added Imports: httr2 to satisfy namespace checks for gemini_generate().

Internal / developer notes

• Consistent prompt skeleton (context → setup/how-to-read → verbatim → output requirements).
• Audience profiles aligned between ANOVA and LinearModel (hierarchy rules, deviation coding, partial vs global tests).
• Safer environment handling for keys: looks up GEMINI_API_KEY by default.

trainer 0.1.0

First CRAN release.

New features

• Audience-aware prompt builders for common analyses:
  • trainer_AovSum() for ANOVA tables (FactoMineR::AovSum).
  • trainer_LinearModel() for multiple linear regression (FactoMineR::LinearModel) with global fit summary + per-term F/T sections and optional AIC/BIC selection.
  • trainer_t_test() for stats::t.test.
  • trainer_var_test() for stats::var.test.
  • trainer_prop_test() for stats::prop.test.
  • trainer_cor_test() for stats::cor.test (Pearson/Spearman/Kendall).
  • trainer_chisq_test() for stats::chisq.test (GOF and contingency tables).
• All trainers support three audiences: “beginner”, “applied”, “advanced”.
  • Consistent language, alpha handling, and no invented numbers.
  • Optional summary_only = TRUE for 3-bullet executive summaries.

Data

• Add three documented datasets for teaching and examples:
  • deforestation: water/air temperatures before vs after riparian clearing.
  • ham: sensory descriptors and overall liking (multiple regression).
  • poussin: chick weight by brooding temperature and sex (ANOVA).

Quality and robustness

• Clear separation of orientation/setup, verbatim output, and output requirements.
• ANOVA T-test section:
  • Correctly re-attaches column header after filtering; clear “(filtered)” title and scope message.
• LinearModel:
  • Includes verbatim model-fit summary (RSE, R², F, p, AIC/BIC).
  • Explains partial (added last) F/T tests and interaction hierarchy.
  • Selection notes (AIC/BIC): kept/dropped RHS terms and deltas (AIC/BIC/RSE/df).
• Proportion and correlation tests:
  • Audience-specific guidance, CI wording, and small-sample cautions.
• Chi-squared test:
  • Branches for GOF vs contingency; notes on Yates correction and Monte Carlo p-values.

Documentation

• Roxygen2 docs for all trainers and datasets.
• Examples guard optional dependencies with requireNamespace("FactoMineR", quietly = TRUE).

Internals

• Shared prompt skeleton via trainer core helpers (audience profile, headers, formatting).
• Consistent phrasing across trainers; improved error checks on inputs.