Package: lmls 0.1.0.9000

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_0.1.0.9000.tar.gz
lmls_0.1.0.9000.zip(r-4.5)lmls_0.1.0.9000.zip(r-4.4)lmls_0.1.0.9000.zip(r-4.3)
lmls_0.1.0.9000.tgz(r-4.4-any)lmls_0.1.0.9000.tgz(r-4.3-any)
lmls_0.1.0.9000.tar.gz(r-4.5-noble)lmls_0.1.0.9000.tar.gz(r-4.4-noble)
lmls_0.1.0.9000.tgz(r-4.4-emscripten)lmls_0.1.0.9000.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'))

Peer review:

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

Datasets:
  • abdom - Abdominal circumference data

On CRAN:

5 exports 3 stars 0.93 score 1 dependencies 17 scripts 199 downloads

Last updated 3 years agofrom:d1b55ba70e. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 26 2024
R-4.5-winOKAug 26 2024
R-4.5-linuxOKAug 26 2024
R-4.4-winOKAug 26 2024
R-4.4-macOKAug 26 2024
R-4.3-winOKAug 26 2024
R-4.3-macOKAug 26 2024

Exports:bootglancelmlsmcmctidy

Dependencies:generics

Location-Scale Regression and the lmls Package

Hannes Riebl

Rendered fromlmls.Rmdusingknitr::rmarkdownon Aug 26 2024.

Last update: 2021-12-29
Started: 2021-06-15