Linear Regression with Non-Constant Variances

Runs a linear regression 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.


Reference manual

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1.2.1 by Marco Nijmeijer, a month ago

Browse source code at

Authors: Posthuma Partners <>

Documentation:   PDF Manual  

GPL-3 license

Imports Matrix, matrixcalc, maxLik, stats

Suggests testthat, knitr, rmarkdown, R.rsp, MASS

See at CRAN