lmls: Gaussian Location-Scale Regression

The Gaussian location-scale regression model is a multi-predictor model with explanatory variables for the mean (= location) and the standard deviation (= scale) of a response variable. This package implements maximum likelihood and Markov chain Monte Carlo (MCMC) inference (using algorithms from Girolami and Calderhead (2011) <doi:10.1111/j.1467-9868.2010.00765.x> and Nesterov (2009) <doi:10.1007/s10107-007-0149-x>), a parametric bootstrap algorithm, and diagnostic plots for the model class.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: generics (≥ 0.1.0)
Suggests: bookdown, coda, covr, ggplot2, knitr, mgcv, mvtnorm, numDeriv, patchwork, rmarkdown, testthat (≥ 3.0.0)
Published: 2022-01-18
Author: Hannes Riebl [aut, cre]
Maintainer: Hannes Riebl <hriebl at uni-goettingen.de>
License: MIT + file LICENSE
URL: https://hriebl.github.io/lmls/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: lmls results


Reference manual: lmls.pdf
Vignettes: Location-Scale Regression and the *lmls* Package


Package source: lmls_0.1.0.tar.gz
Windows binaries: r-devel: lmls_0.1.0.zip, r-release: lmls_0.1.0.zip, r-oldrel: lmls_0.1.0.zip
macOS binaries: r-release (arm64): lmls_0.1.0.tgz, r-oldrel (arm64): lmls_0.1.0.tgz, r-release (x86_64): lmls_0.1.0.tgz, r-oldrel (x86_64): lmls_0.1.0.tgz


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