SLTCA: Scalable and Robust Latent Trajectory Class Analysis

Conduct latent trajectory class analysis with longitudinal data. Our method supports longitudinal continuous, binary and count data. For more methodological details, please refer to Hart, K.R., Fei, T. and Hanfelt, J.J. (2020), Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics <doi:10.1111/biom.13366>.

Version: 0.1.0
Depends: R (≥ 3.3.0)
Imports: stats, geepack, VGAM, Matrix, mvtnorm
Published: 2020-09-23
Author: Kari Hart [aut], Teng Fei ORCID iD [cre, aut], John Hanfelt ORCID iD [aut]
Maintainer: Teng Fei <tfei at emory.edu>
BugReports: https://github.com/tengfei-emory/SLTCA/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: SLTCA results

Downloads:

Reference manual: SLTCA.pdf
Package source: SLTCA_0.1.0.tar.gz
Windows binaries: r-devel: SLTCA_0.1.0.zip, r-release: SLTCA_0.1.0.zip, r-oldrel: SLTCA_0.1.0.zip
macOS binaries: r-release: SLTCA_0.1.0.tgz, r-oldrel: SLTCA_0.1.0.tgz

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