Package: lmls 0.1.1
Hannes Riebl
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.
Authors:
lmls_0.1.1.tar.gz
lmls_0.1.1.tar.gz(r-4.5-noble)lmls_0.1.1.tar.gz(r-4.4-noble)
lmls_0.1.1.tgz(r-4.4-emscripten)lmls_0.1.1.tgz(r-4.3-emscripten)
lmls.pdf |lmls.html✨
lmls/json (API)
NEWS
| # Install 'lmls' in R: |
| install.packages('lmls', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hriebl/lmls/issues
- abdom - Abdominal circumference data
Last updated 5 days agofrom:2461c44a27. Checks:OK: 1 WARNING: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 25 2024 |
| R-4.5-linux | WARNING | Nov 25 2024 |
Exports:bootglancelmlsmcmctidy
Dependencies:generics
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Abdominal circumference data | abdom |
| Parametric bootstrap for LMLS | boot |
| Gaussian location-scale regression | lmls |
| Methods for LMLS | coef.lmls fitted.lmls lmls-methods predict.lmls residuals.lmls vcov.lmls |
| MCMC inference for LMLS | mcmc |
| Summary for LMLS | summary.lmls |
