Package: binaryMM 0.1.1

binaryMM: Flexible Marginalized Models for Binary Correlated Outcomes

Estimates marginalized mean and dependence model parameters for correlated binary response data. Dependence model may include transition and/or latent variable terms. Methods are described in: Schildcrout and Heagerty (2007) <doi:10.1111/j.1541-0420.2006.00680.x>, Heagerty (1999) <doi:10.1111/j.0006-341x.1999.00688.x>, Heagerty (2002) <doi:10.1111/j.0006-341x.2002.00342.x>.

Authors:Jonathan Schildcrout [aut], Nathaniel Mercaldo [aut], Chiara Di Gravio [cre]

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binaryMM.pdf |binaryMM.html
binaryMM/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/chiaradg/binarymm/issues

Datasets:
  • datrand - Simulated data set
  • madras - Madras Longitudinal Schizophrenia Study: Thought Disorder Subset

On CRAN:

3.70 score 3 scripts 218 downloads 4 exports 3 dependencies

Last updated 2 years agofrom:393e88eb0a. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 11 2024
R-4.5-win-x86_64OKNov 11 2024
R-4.5-linux-x86_64OKNov 11 2024
R-4.4-win-x86_64OKNov 11 2024
R-4.4-mac-x86_64OKNov 11 2024
R-4.4-mac-aarch64OKNov 11 2024
R-4.3-win-x86_64OKNov 11 2024
R-4.3-mac-x86_64OKNov 11 2024
R-4.3-mac-aarch64OKNov 11 2024

Exports:GenBinaryYget.GHmmMMLongit

Dependencies:fastGHQuadMASSRcpp

binaryMM: Fitting Flexible Marginalized Models for Binary Correlated Outcomes

Rendered frommy-vignette.Rmdusingknitr::rmarkdownon Nov 11 2024.

Last update: 2022-09-19
Started: 2022-09-17