panglm 1.1.4

• Added \value documentation to confint.panglm(), fitted.panglm(), summary.panglm(), and vcov.panglm(), describing the structure and meaning of each return value (CRAN resubmission).

panglm 1.1.3

• Added method references (Hausman 1978, Chamberlain 1980, Allison and Waterman 2002, Croissant and Millo 2008) to the DESCRIPTION as <doi:...> links.

panglm 1.1.2

Bug fixes

• model = "random", family = "gaussian" always reported logLik/AIC/ BIC as NA, even though fit_random_gaussian() already estimates the Swamy-Arora variance components (sigma_v2, sigma_mu2) needed to compute a real likelihood - they were simply discarded. Added the closed-form Gaussian random-intercept log-likelihood (Sherman-Morrison identity on Omega_i = sigma_v2 * I + sigma_mu2 * J), using the already- estimated coefficients and variance components. Cross-validated: on the bundled copd data, logLik(random) closely tracks logLik(pooling) on the same formula, as expected since random-effects GLS nests pooled OLS as sigma_mu2 -> 0.
• model = "within" (one-way and two-way) had the same loglik = NA_real_ gap. By the Frisch-Waugh-Lovell theorem the demeaned-regression residuals are algebraically identical to the full dummy-variable-regression residuals, so the standard OLS ML Gaussian log-likelihood applies directly - added, using the npar (regressors + fixed-effect count + 1 dispersion parameter) that panglm_parameter_count() already computed for these branches but previously had no likelihood to pair with.

New features

• glance.panglm() now reports r.squared/adj.r.squared (computed from fitted.values()/y) for every model type; previously not reported at all.

Bug fixes

• panglm(model = "random", effect = "individual", family = "gaussian") printed a raw solve(): system is singular; attempting approx solution from Armadillo whenever a covariate was time-invariant (e.g. baseline sex alongside a pre/post indicator) or the panel was balanced with a deterministic time indicator (e.g. any two-wave pre/post design) - both make the intermediate within/between regressions used to estimate the Swamy-Arora variance components exactly rank-deficient. The estimates were already correct (verified against lme4::lmer), but the warning bypassed R’s condition system entirely. Switched those two intermediate regressions to stats::lm.fit(), which handles rank deficiency via pivoted QR the same way lm() does, with no near-singular solve and no raw stderr output.

panglm 1.1.0

CRAN submission candidate. Adds a bundled synthetic dataset, copd (a simulated COPD follow-up panel), and runnable @examples to every exported function and documented method.

• Added plot.panglm() for coefficient, residual, and fitted-value views, and plot.panglm_hurdle() for the two hurdle components.
• Added zero-truncated NB2 positive-count models to panglm_hurdle() through count_family = "negbin".
• Added HC1 and cluster-robust covariance estimates for one-way conditional binomial fixed-effects models.
• Reorganized the package vignette as a methodological account of model specification, estimation, inference, diagnostics, and scope.
• Completed fitted-value and prediction semantics across estimators. Random-effects response predictions integrate over the fitted mixing distribution, fixed-effects predictions include known panel effects, and exact conditional binomial fits expose conditional inclusion probabilities. Fitted values and residuals now preserve the input row order for unsorted panels.
• Corrected likelihood parameter accounting. logLik() now counts fitted dispersion, shape, variance, and absorbed fixed-effect parameters where they belong to the reported likelihood. Gaussian pooled likelihoods use the maximum-likelihood variance while retaining the unbiased residual variance for covariance estimation. glance() now reports AIC, BIC, and the likelihood degrees of freedom.
• Corrected sandwich inference for exact conditional binomial models. HC1 now operates on independent stratum scores, and custom clusters are required to preserve complete conditioned strata. Custom cluster vectors are now correctly reordered when a panel was supplied out of index order.
• Revised the fixed-effects NB2 description to state that the dummy-variable estimator remains subject to finite-T incidental-parameter concerns in short panels.
• Hardened model-frame handling. Factor interactions are retained, unidentified columns are reported and removed before numerical fitting, na.exclude restores fitted values and residuals to the input length, and panel indexes are checked for missing or duplicate individual-time keys. Nontrivial weights and nonzero offsets now fail explicitly instead of risking an apparently successful fit that ignores them.

panglm 0.5.0

First CRAN submission candidate. Consolidates the estimators, inference, and zero-inflated-count support added since 0.1.0 (see below); no functional changes since the last version bump beyond package-metadata and documentation cleanup for submission (URL/BugReports fields, vignette housekeeping).

panglm 0.1.0

Initial release.

Estimators

• model = "pooling": gaussian/poisson/binomial GLM via IRLS, and genuine NB2 (negative binomial) via theta-profiled IRLS – matches MASS::glm.nb() exactly. (family = "negbin" previously silently aliased to Poisson at every model level; this is fixed everywhere below.)
• model = "within": gaussian (exact demeaning, one-way or effect = "twoways"), poisson (conditional MLE, one-way or effect = "twoways" via outer-IRLS/inner-weighted-FWL – the algorithm behind fixest::feglm()), negative binomial (Allison & Waterman 2002 unconditional dummy-variable estimator – matches fixest::fenegbin() exactly; tractable for small-to-moderate N, not intended for very large panels), and binomial (exact conditional logistic regression, Chamberlain 1980 – matches survival::clogit(method = "exact"), no pglm equivalent). effect = "twoways" is available for gaussian, poisson, and negbin (the latter two via outer-IRLS/inner-weighted-FWL, matching fixest::fenegbin() exactly); not yet for binomial (no general closed-form two-way conditional logit exists).
• Groups with an all-zero outcome are now dropped (with an informative message()) before fitting the Allison-Waterman FE-NB2 estimator – their dummy-variable intercept is unbounded under the log link, the same boundary case model = "within", family = "binomial" already screened for. Previously left in, this could contaminate the shared covariate coefficients via the joint solve, not just the offending group’s own intercept.
• model = "random": gaussian (Swamy-Arora), poisson (Poisson-Gamma, a single dispersion parameter), negative binomial (a genuinely different, 2-parameter beta-negative-binomial mixture matching pglm’s lnl.negbin.R exactly – previously aliased to the Poisson-Gamma model), and binomial (Gauss-Hermite quadrature) random effects. The random-effects Poisson fit now seeds from the pooled-Poisson MLE plus a method-of-moments dispersion estimate, and uses Levenberg-Marquardt/ridge damping (with a warning when it engages) instead of erroring on a singular Hessian – verified on heavily zero-inflated, extremely skewed panels that previously crashed.

Inference

• vcov(fit, type = "HC1" | "cluster") for pooled/within models, including effect = "twoways" (gaussian) and within negbin (Allison-Waterman): the sandwich correction uses the full covariate+dummy score and bread (all (k+G) parameters), not just the covariate block, since the dummy intercepts are jointly estimated and contribute sampling variability of their own. Random-effects models intentionally keep model-based (information-matrix) SEs only, matching lme4/glmmTMB convention.
• confint.panglm() – Wald intervals using whichever vcov the fit currently carries.
• panglm_dispersiontest() – Pearson chi-squared/df overdispersion test for poisson/negbin fits with available fitted.values. Observations with a zero or unavailable fitted value (e.g. rows in an all-zero group screened out of a fixed-effects negbin fit) are excluded from the statistic rather than producing a 0/0 = NaN; the excluded count is reported as n_excluded on the returned object.
• panglm_hausman() for FE-vs-RE specification testing.
• tidy()/glance() methods registered against generics::tidy/glance for broom/modelsummary compatibility.

Zero-inflated counts

• panglm_hurdle(): a fixed-effects hurdle model for panels with structural zeros (a pattern that shows up as extreme, family-invariant overdispersion under either family = "poisson" or family = "negbin" in panglm()). Fits 1(y>0) via the existing conditional-logit FE estimator, and a zero-truncated Poisson or NB2 FE model (an Allison-Waterman-style dummy-variable estimator, Newton-Raphson with exact Fisher information) on the y>0 subsample. The truncated-Poisson math (no fixed effects) matches pscl::hurdle()’s count part exactly. Both zero-truncated likelihoods are validated against pscl::hurdle().

Known limitations

• effect = "twoways" not yet available for binomial (no general closed-form two-way conditional logit exists).
• No between effects.
• No ordinal/tobit families.
• Not yet on CRAN.