Package: robustBLME 0.1.3

Erlis Ruli

robustBLME: Robust Bayesian Linear Mixed-Effects Models using ABC

Bayesian robust fitting of linear mixed effects models through weighted likelihood equations and approximate Bayesian computation as proposed by Ruli et al. (2017) <arxiv:1706.01752>.

Authors:Erlis Ruli [aut, cre], Nicola Sartori [aut], Laura Ventura [aut]

robustBLME_0.1.3.tar.gz
robustBLME_0.1.3.tar.gz(r-4.5-noble)robustBLME_0.1.3.tar.gz(r-4.4-noble)
robustBLME_0.1.3.tgz(r-4.4-emscripten)robustBLME_0.1.3.tgz(r-4.3-emscripten)
robustBLME.pdf |robustBLME.html✨
robustBLME/json (API)

# Install 'robustBLME' in R:
install.packages('robustBLME', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/erlisr/robustblme/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • ergoStool - Ergometrics experiment with stool types

On CRAN:

Conda:

openblascpp

1.70 score 148 downloads 4 exports 20 dependencies

Last updated 7 years agofrom:aa548f112d. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 22 2025
R-4.5-linux-x86_64OKMar 22 2025
R-4.4-linux-x86_64OKMar 22 2025

Exports:hpdkdeFSBTrblmetune.h

Dependencies:bootcodetoolsdoParallelforeachiteratorslatticelme4MASSMatrixminqamvtnormnlmenloptrnumDerivrbibutilsRcppRcppArmadilloRcppEigenRdpackreformulas

Citation

To cite robustBLME in publications use:

Ruli E., Sartori N. and Ventura L. (2017). Fitting Robust Bayesian Linear Mixed-Effects Models Using robustBLME Journal of Statistical Software, xx(xx), xx-xx. URL http://www.jstatsoft.org.

Corresponding BibTeX entry: 

  @Article{,
    title = {Fitting Robust Bayesian Linear Mixed-Effects Models Using
      {robustBLME}},
    author = {Erlis Ruli and Nicola Sartori and Laura Ventura},
    journal = {Journal of Statistical Software},
    year = {2017},
    volume = {xx},
    number = {xx},
    pages = {xx--xx},
    url = {http://www.jstatsoft.org},
  }

Readme and manuals

robustBLME

This is an R implementation of the method proposed by Ruli et al. (2017). It gives routines for Bayesian robust fitting of linear mixed effects models though weighted likelihood equations and approximate Bayesian computation.