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 + file LICENSE |
| URL: |
https://hriebl.github.io/lmls/, https://github.com/hriebl/lmls |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
lmls results |
Documentation:
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