Package: mlxR 4.2.2

mlxR: Simulation of Longitudinal Data

Simulation and visualization of complex models for longitudinal data. The models are encoded using the model coding language 'Mlxtran' and automatically converted into C++ codes. That allows one to implement very easily complex ODE-based models and complex statistical models, including mixed effects models, for continuous, count, categorical, and time-to-event data.

Authors:Marc Lavielle [aut, cre], Esther Ilinca [ctb], Raphael Kuate [ctb]

mlxR_4.2.2.tar.gz
mlxR_4.2.2.zip(r-4.5)mlxR_4.2.2.zip(r-4.4)mlxR_4.2.2.zip(r-4.3)
mlxR_4.2.2.tgz(r-4.5-any)mlxR_4.2.2.tgz(r-4.4-any)mlxR_4.2.2.tgz(r-4.3-any)
mlxR_4.2.2.tar.gz(r-4.5-noble)mlxR_4.2.2.tar.gz(r-4.4-noble)
mlxR_4.2.2.tgz(r-4.4-emscripten)mlxR_4.2.2.tgz(r-4.3-emscripten)
mlxR.pdf |mlxR.html
mlxR/json (API)

# Install 'mlxR' in R:
install.packages('mlxR', repos = c('https://marclavielle.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/marclavielle/mlxr/issues

On CRAN:

Conda:

6.28 score 19 stars 67 scripts 297 downloads 16 mentions 19 exports 28 dependencies

Last updated 2 years agofrom:b2a572e27a. Checks:3 OK, 6 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 31 2025
R-4.5-winNOTEMar 31 2025
R-4.5-macNOTEMar 31 2025
R-4.5-linuxNOTEMar 31 2025
R-4.4-winNOTEMar 31 2025
R-4.4-macNOTEMar 31 2025
R-4.4-linuxNOTEMar 31 2025
R-4.3-winOKMar 31 2025
R-4.3-macOKMar 31 2025

Exports:catplotmlxexposureggplotmlxinitMlxRinlineDataFrameinlineModelkmplotmlxlixoft.read.tablemlxploremonolix2simulxpkmodelprctilemlxread.vectorreadDatamlxshinymlxsimpopmlxsimulxstatmlxwriteDatamlx

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr