fgls 0.1.0

First release. A port of the Python package ‘pyfgls’ (0.1.0), validated against it (golden fixtures and a cross-language comparison on 4,000 simulated data sets).

Estimators

• fgls(): feasible GLS correcting for serial correlation (Perron and González-Coya, 2026), heteroskedasticity of unknown form (González-Coya and Perron, 2024) or both (Supplement S-4, “F-CH”), with a formula interface and ts/zoo/xts/time-indexed data frames.
• serial_fgls(): Durbin-regression AR filter with BIC/AIC lag selection on a common sample, quasi-differencing, robust (eqs. 28-29) and naive (eq. 27) variances, HC variants, Wallis recovery for lagged dependent variables, and the GMA estimator for MA(1) errors.
• lasso_fgls(): adaptive-Lasso skedastic function (ridge first step, joint cross-validation of the penalty and the weight exponent with common folds, exact Lasso path), SVR and Romano-Wolf WLS-S1/S2 alternatives, known variances, and the bias-corrected sandwich of eqs. (6)-(8).
• Bootstrap covariances (sieve and wild) as a cross-check.

Benchmarks and tools

• ols_robust() (Andrews quadratic-spectral HAC, Newey-West, HC0-HC3), cochrane_orcutt(), durbin_estimator(), infeasible_gls(), hybrid_gls().
• compare_fgls(), summary(), confint(), predict(), plot() and the other usual methods; works with lmtest::coeftest() and the broom generics.
• Diagnostics: test_predetermined() (experimental), adf_test(), weight_diagnostics(), ar_roots(), nonexogeneity_share().
• Simulation designs of both papers: simulate_serial(), simulate_hetero(), simulate_combined(), simulate_predictive().

Differences from pyfgls 0.1.0

Estimates and default variances agree with pyfgls (see vignette("validation")). Elsewhere the R package deliberately differs:

• nonexogeneity_share is computed from the robust variance (PG26 eqs. 28-29) whatever vcov is requested (pyfgls reports 0 for non-robust types), and is NA for the benchmark estimators and the GMA estimator, which do not compute it.
• durbin_estimator() recovers the coefficients on regressors whose lags are collinear with the regressors (constant, trends, periodic dummies) as ((I - _j _j P_j)^{-1} c), e.g. (c / (1 - _j _j)) for the intercept; pyfgls reports the Durbin coefficient (c).
• The kmax cap and the ar_order check count the Durbin regressors that are actually kept, so dropped lags of trends and seasonal dummies no longer shrink kmax. Without such columns the cap is the pyfgls one.
• serial = "ma1" rejects ar_order, kmax, lagged_dep_vars and vcov = "bootstrap" instead of ignoring them.
• Formula-interface behavior with no pyfgls counterpart: subset, na.action (including na.exclude padding), row-numbered error messages, and checks of time-series row order and time indexes.