Package: lmls 0.1.1

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:Hannes Riebl [aut, cre]

lmls_0.1.1.tar.gz
lmls_0.1.1.zip(r-4.5)lmls_0.1.1.zip(r-4.4)lmls_0.1.1.zip(r-4.3)
lmls_0.1.1.tgz(r-4.5-any)lmls_0.1.1.tgz(r-4.4-any)lmls_0.1.1.tgz(r-4.3-any)
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://hriebl.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hriebl/lmls/issues

Pkgdown site:https://hriebl.github.io

Datasets:
  • abdom - Abdominal circumference data

On CRAN:

Conda-Forge:

4.65 score 3 stars 15 scripts 620 downloads 5 exports 1 dependencies

Last updated 4 months agofrom:16f8568877. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 11 2025
R-4.5-winOKFeb 11 2025
R-4.5-macOKFeb 11 2025
R-4.5-linuxOKFeb 11 2025
R-4.4-winOKFeb 11 2025
R-4.4-macOKFeb 11 2025
R-4.3-winOKFeb 11 2025
R-4.3-macOKFeb 11 2025

Exports:bootglancelmlsmcmctidy

Dependencies:generics

Location-Scale Regression and the lmls Package

Hannes Riebl

Rendered fromlmls.Rmdusingknitr::rmarkdownon Feb 11 2025.

Last update: 2024-11-12
Started: 2021-06-15