Package: hcinfer 0.1.0.9000

hcinfer: Heteroskedasticity-Consistent Inference for Linear Models

Computes heteroskedasticity-consistent covariance matrix estimators for ordinary least squares regression models. The published HC0 through HC5m estimators implemented in the package follow White (1980) <doi:10.2307/1912934>, Hinkley (1977) <doi:10.1080/00401706.1977.10489550>, Horn et al. (1975) <doi:10.1080/01621459.1975.10479877>, MacKinnon and White (1985) <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004) <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto and da Silva (2011) <doi:10.1007/s10182-010-0141-2>, Cribari-Neto et al. (2007) <doi:10.1080/03610920601126589>, and Li et al. (2016) <doi:10.1080/00949655.2016.1198906>. The package also includes HCbeta, a new estimator proposed by the package authors. It provides normal Wald tests, confidence intervals, diagnostics, and S3 output for applied inference.

Authors:Pedro Rafael D. Marinho [aut, cre], Francisco Cribari-Neto [aut], Marina Oliveira Cunha [aut]

hcinfer_0.1.0.9000.tar.gz
hcinfer_0.1.0.9000.zip(r-4.7)hcinfer_0.1.0.9000.zip(r-4.6)hcinfer_0.1.0.9000.zip(r-4.5)
hcinfer_0.1.0.9000.tgz(r-4.6-any)hcinfer_0.1.0.9000.tgz(r-4.5-any)
hcinfer_0.1.0.9000.tar.gz(r-4.7-any)hcinfer_0.1.0.9000.tar.gz(r-4.6-any)
hcinfer_0.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hcinfer/json (API)

# Install 'hcinfer' in R:
install.packages('hcinfer', repos = c('https://prdm0.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/prdm0/hcinfer/issues

Pkgdown/docs site:https://prdm0.github.io

Datasets:

On CRAN:

Conda:

3.92 score 1 stars 14 scripts 4 exports 23 dependencies

Last updated from:18c63cd9f1. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK150
source / vignettesOK169
linux-release-x86_64OK303
macos-release-arm64OK106
macos-oldrel-arm64OK103
windows-develOK81
windows-releaseOK123
windows-oldrelOK100
wasm-releaseOK111

Exports:hc_methodshcinfertestsvcov_hc

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecyclemagrittrpillarpkgconfigpurrrR6RColorBrewerrlangS7scalestibbleutf8vctrsviridisLitewithr

HC Estimator Methodology
The model and the target covariance matrix | The sandwich form | The hat matrix and leverage | Residual compression | The classical HC family | HC5 and HC5m | Overshooting | HCbeta motivation | HCbeta construction | Step 1: truncate leverage complements | Step 2: estimate Beta shape parameters by moments | Step 3: shrink toward the uniform cdf | Step 4: use a decaying exponent | Normal Wald inference | Practical guidance | Implementation notes | References
Rendered fromhcinfer-methodology.Rmdusingknitr::rmarkdown

Last update: 2026-06-05
Started: 2026-06-02

Introduction to hcinfer
Data and Model | Available Estimators | Robust Inference | Test Extraction | Confidence Interval Extraction | Coefficients and Covariance Matrices | Covariance-Only Workflow | Plots | A Small Comparison | Typical Workflow
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2026-05-28
Started: 2026-05-27

Comparing HC Estimators
List the available estimators | Fit one model | Run several methods | Compare one coefficient | Plot the robust standard errors | Compare confidence intervals directly | Compare covariance objects | Compare diagnostics across methods | What to report
Rendered fromhcinfer-comparison.Rmdusingknitr::rmarkdown

Last update: 2026-05-27
Started: 2026-05-27

Using HCbeta
Run HCbeta | Extract coefficient test results | Inspect HCbeta parameters | Inspect leverage and weights | Use the covariance-only interface | Run a sensitivity check | Compare HCbeta with one classical estimator | Practical workflow
Rendered fromhcinfer-hcbeta.Rmdusingknitr::rmarkdown

Last update: 2026-05-27
Started: 2026-05-27