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: | 0.1.1 |
| 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: | 2024-11-20 |
| DOI: | 10.32614/CRAN.package.lmls |
| Author: | Hannes Riebl [aut, cre] |
| Maintainer: | Hannes Riebl <hriebl at posteo.de> |
| BugReports: | https://github.com/hriebl/lmls/issues |
| License: | MIT + fileLICENSE |
| URL: | https://hriebl.github.io/lmls/,https://github.com/hriebl/lmls |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | lmls results |
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