NEWS

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hcinfer 0.3.0 (2026-08-25)

• Added gls_mult() for feasible generalized least squares estimation under multiplicative heteroskedasticity. The estimator argument chooses the fit, either "ml" (default, Gaussian maximum likelihood) or "two_step" (Harvey's corrected auxiliary regression), and for maximum likelihood the method argument selects the stats::optim() algorithm: "BFGS" (default), "Nelder-Mead", "CG", or "L-BFGS-B". New coef() and vcov() methods access the mean and dispersion coefficients via model =, and confint(), tests(), summary(), print(), fitted(), and residuals() methods support applied inference, while logLik() and nobs() enable AIC() and BIC() for maximum likelihood fits. The package Description now also covers feasible generalized least squares following Harvey (1976) and Cribari-Neto and Pereira (2019).
• Corrected the HC5 adjustment factor, which now follows the erratum to Cribari-Neto, Souza and Vasconcellos (2007). vcov_hc() and hcinfer() compute g_t = (1 - h_t)^(-delta_t / 2) instead of the expression g_t = (1 - h_t)^(-delta_t) printed in Section 3 of the original article, keeping delta_t = min(h_t / h_bar, max(4, k * h_max / h_bar)) and the default k = 0.7 unchanged. HC5 adjustment factors are therefore smaller, and HC5 standard errors, Wald statistics, p-values, and confidence intervals differ from those returned by earlier versions. HC5m is unaffected because it follows Li, Zhang, Zhang and Wang (2016) and applies its own exponent without the factor 1/2, so type = "hc5m" with k1 = 0, k2 = 0, and k3 = 1 no longer reproduces type = "hc5" and instead squares its adjustment factor.
• Documented the corrected HC5 expression in the vcov_hc() help page and in the methodology vignette, and added the 2008 erratum to the references of hcinfer(), vcov_hc(), tests(), the methodology vignette, and the package Description.

hcinfer 0.2.0 (2026-08-04)

• Added boot_pairs() for pairs (case) bootstrap standard errors and confidence intervals of ordinary least squares coefficients. It resamples the observations with replacement, refits the model on each replicate, and summarizes the sampling distribution of the coefficients, providing an assumption-free empirical reference for the analytic heteroskedasticity-consistent standard errors from hcinfer() and vcov_hc(). Percentile, basic, and normal intervals are available, the resampling is reproducible through the seed argument, and the replicate fits can optionally run in parallel via purrr::in_parallel() and mirai without changing the numeric result.
• Added coef(), vcov(), confint(), print(), and plot() methods for the hcinfer_boot objects returned by boot_pairs(). vcov() returns the bootstrap covariance matrix of the coefficients, confint() can recompute intervals at a different level or type directly from the stored replicates, and plot() draws the bootstrap confidence intervals, coloring each coefficient by whether its interval excludes or includes zero.
• hcinfer() and vcov_hc() now accept independent HCbeta shape caps from 50 through 25000 inclusive, with defaults of 10000. HC0, HC1, and HCbeta also remain defined for an exact leverage value of one, while HC2, HC3, HC4, HC4m, HC5, and HC5m retain the positive leverage-complement requirement.
• hcinfer() and vcov_hc() now enforce the fixed HCbeta shape floor of 0.01 after shrinkage and before the upper caps, including for nondefault leverage-complement truncation limits. The shape floor remains fixed when lower changes and is not a method argument.

hcinfer 0.1.1 (2026-07-13)

• Added the PublicSchools2 dataset with 2024 per capita income, 2025 public school expenditure per student, a Southern-region indicator, and complete variable and source documentation.
• Standardized the federal district name in PublicSchools from Washington DC to District of Columbia.

hcinfer 0.1.0 (2026-06-10)

hcinfer 0.0.0.9000

• Added the initial development version with HC covariance estimators, normal Wald inference, S3 output, and the PublicSchools dataset.
• plot() now supports vcov_hc() objects, producing leverage-versus-adjustment-factor graphics for inspecting the relationship between h_t and g_t.
• summary() now prints formal test results, confidence interval checks, and optional emoji markers to improve interpretation of robust inference output.
• summary() now keeps displayed test_result decisions consistent with numeric p-values when p-values are displayed as <0.001.
• Added tests() as a formal extractor for coefficient-level Wald test results. The function mirrors the API of confint(): an optional parm argument selects coefficients by name or position, and an optional alpha argument recomputes the reject column without affecting the stored p-values or test statistics.