OLSengine 1.2.0

Bug fixes / corrections

did_engine(): the pre-treatment placebo check is now correctly labelled as a pre-treatment balance check rather than a parallel-trends test. A single pre-treatment period cannot establish parallel trends, which requires observing group trajectories across multiple periods. No change to the underlying statistical test.

anova_engine(): when non_parametric = “auto” and Shapiro-Wilk rejects normality, Levene’s test (Brown-Forsythe) now informs the correction: clear heteroscedasticity (p < .01) routes to Welch’s ANOVA instead of Kruskal-Wallis; ambiguous cases (.01-.10) retain the previous default with an explicit warning; homogeneous variance (p > .10) confirms genuine non-normality as before. Validated via Monte Carlo simulation (see harness/ for reproducible scripts).

iv_engine(): fixed an O(n^2) memory/time bug caused by explicitly forming the full n x n projection matrix when computing the second-stage variance. The offending step now computes X_tilde via associativity instead, giving O(n*k) memory. Previously this failed with “cannot allocate vector” errors around n = 100,000; it now runs in a fraction of a second at that size.
anova_engine(): fixed a hard crash for n > 5,000, where shapiro.test() errors out (“sample size must be between 3 and 5000”). It now falls back to a Kolmogorov-Smirnov test above that threshold, matching the behavior ols_engine() already had.
panel_engine(): fixed the internal Hausman test statistic, which did not match plm::phtest(). The between estimator used to construct the GLS transformation parameter (theta) was missing its intercept and was not isolating the idiosyncratic variance component, and the covariance-matrix positive-definiteness safeguard did not match the reference algorithm used by plm::phtest() (which inverts directly and takes the absolute value of the resulting statistic, without a positive-definiteness check). In some scenarios this could select the theoretically inconsistent estimator (Random Effects when Fixed Effects was actually warranted). See the “Statistical correctness note” in this release’s cran-comments.md for scope and mitigation.

Added panel data estimation (model = "panel" in paper_engine()): fixed effects and random effects estimators with automatic Hausman-test-based selection (method = "auto", "fe", or "re"), plus balanced/unbalanced panel diagnostics.
Added instrumental variables estimation (model = "iv"): two-stage least squares (2SLS) in pure base R, with first-stage weak-instrument diagnostics.
Added difference-in-differences estimation (model = "did"): treatment x post-period interaction model with parallel-trends diagnostics.
plot_engine() gains matching plot types for the three new models: a forest plot for panel estimates, a forest plot for 2SLS coefficients, and a parallel-trends plot for DiD.
Added the academic_salaries example dataset, used in the new panel/IV/DiD vignette sections.

Expanded vignette_tutorial.Rmd with worked examples for panel data, IV, and DiD models.
Updated paper_engine() and plot_engine() documentation for the new parameters (entity_id, time_id, method, instruments, treatment_var, time_var, treatment_level, post_level).
Updated README to reflect the six available estimation engines.

Initial CRAN release: OLS regression, ANOVA/t-tests, and logistic regression, with the integrated “Methodological Customs” diagnostics layer and APA-formatted output tables and plots.