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  [cre,
aut],
John Hanfelt
[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 |
Documentation:
| Reference manual: | SLTCA.pdf |
Downloads:
| 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 (arm64): SLTCA_0.1.0.tgz, r-oldrel (arm64): SLTCA_0.1.0.tgz, r-release (x86_64): SLTCA_0.1.0.tgz, r-oldrel (x86_64): SLTCA_0.1.0.tgz |
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