Package: jmcm 0.2.4

Jianxin Pan

jmcm: Joint Mean-Covariance Models using 'Armadillo' and S4

Fit joint mean-covariance models for longitudinal data. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Armadillo' C++ library for numerical linear algebra and 'RcppArmadillo' glue.

Authors:Jianxin Pan [aut, cre], Yi Pan [aut]

jmcm_0.2.4.tar.gz
jmcm_0.2.4.tar.gz(r-4.5-noble)jmcm_0.2.4.tar.gz(r-4.4-noble)
jmcm_0.2.4.tgz(r-4.4-emscripten)jmcm_0.2.4.tgz(r-4.3-emscripten)
jmcm.pdf |jmcm.html
jmcm/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/ypan1988/jmcm/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

openblascpp

2.48 score 1 packages 9 scripts 905 downloads 13 exports 4 dependencies

Last updated 4 years agofrom:466b403c74. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 16 2024
R-4.5-linux-x86_64NOTEDec 16 2024

Exports:acd_estimationbootcurvegetJMCMhpc_estimationjmcmjmcmControlldFormulamcd_estimationmeanplotmkJmcmModoptimizeJmcmregressogramshow

Dependencies:FormulaRcppRcppArmadilloroptim

jmcm: An R Package for Joint Mean-Covariance Modelling of Longitudinal Data

Rendered fromjss2542.pdf.asisusingR.rsp::asison Dec 16 2024.

Last update: 2018-11-10
Started: 2018-11-10