lmvar: Linear Regression with Non-Constant Variances

Runs a linear-like regression with in which both the expected value and the variance can vary per observation. The expected values mu follows the standard linear model mu = X_mu * beta_mu. The standard deviation sigma follows the model log(sigma) = X_sigma * beta_sigma. The package comes with two vignettes: 'Intro' gives an introduction, 'Math' gives mathematical details.

Version: 1.5.2
Imports: Matrix (≥ 1.2-4), matrixcalc (≥ 1.0-3), maxLik (≥ 1.3-4), stats (≥ 3.2.5), parallel (≥ 3.3.0), graphics (≥ 3.3.0), grDevices (≥ 3.3.0)
Suggests: testthat, knitr, rmarkdown, R.rsp, MASS, plotly (≥ 4.7.1)
Published: 2019-05-16
Author: Posthuma Partners
Maintainer: Marco Nijmeijer <nijmeijer at posthuma-partners.nl>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: lmvar results


Reference manual: lmvar.pdf
Vignettes: Introduction to the package
Math details
Package source: lmvar_1.5.2.tar.gz
Windows binaries: r-devel: lmvar_1.5.2.zip, r-release: lmvar_1.5.2.zip, r-oldrel: lmvar_1.5.2.zip
macOS binaries: r-release: lmvar_1.5.2.tgz, r-oldrel: lmvar_1.5.2.tgz
Old sources: lmvar archive


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