Package: GPvam 3.2-0

GPvam: Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling

An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.

Authors:Andrew Karl [cre, aut], Yan Yang [aut], Sharon Lohr [aut]

GPvam_3.2-0.tar.gz
GPvam_3.2-0.zip(r-4.7)GPvam_3.2-0.zip(r-4.6)GPvam_3.2-0.zip(r-4.5)
GPvam_3.2-0.tgz(r-4.6-x86_64)GPvam_3.2-0.tgz(r-4.6-arm64)GPvam_3.2-0.tgz(r-4.5-x86_64)GPvam_3.2-0.tgz(r-4.5-arm64)
GPvam_3.2-0.tar.gz(r-4.7-arm64)GPvam_3.2-0.tar.gz(r-4.7-x86_64)GPvam_3.2-0.tar.gz(r-4.6-arm64)GPvam_3.2-0.tar.gz(r-4.6-x86_64)
GPvam_3.2-0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
GPvam/json (API)
NEWS

# Install 'GPvam' in R:
install.packages('GPvam', repos = c('https://akarl46556.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.00 score 5 scripts 180 downloads 5 exports 24 dependencies

Last updated from:b9b7bd2f49. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK172
linux-devel-x86_64OK160
source / vignettesOK193
linux-release-arm64OK158
linux-release-x86_64OK148
macos-release-arm64OK142
macos-release-x86_64OK328
macos-oldrel-arm64OK124
macos-oldrel-x86_64OK246
windows-develOK173
windows-releaseOK137
windows-oldrelOK122
wasm-releaseOK108

Exports:bias.test.customGPvamplot.GPvamprint.GPvamsummary.GPvam

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglatticelifecycleMASSMatrixnumDerivpatchworkR6RColorBrewerRcppRcppArmadillorlangS7scalesvctrsviridisLitewithr