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Fitting multivariate response models with random effects on one or two levels; whereby the (one-dimensional) random effect represents a latent variable approximating the multivariate space of outcomes, after possible adjustment for covariates. The method is particularly useful for multivariate, highly correlated outcome variables with unobserved heterogeneities. Applications include regression with multivariate responses, as well as multivariate clustering or ranking problems. See Zhang and Einbeck (2024) <doi:10.1007/s42519-023-00357-0>.
| Version: | 0.1.7 |
| Depends: | R (≥ 3.5.0) |
| Imports: | mvtnorm, stats, matrixStats, utils, lme4 |
| Published: | 2024-03-21 |
| Author: | Yingjuan Zhang [aut, cre], Jochen Einbeck [aut, ctb] |
| Maintainer: | Yingjuan Zhang <yingjuan.zhang at durham.ac.uk> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | mult.latent.reg results |
| Reference manual: | mult.latent.reg.pdf |
| Package source: | mult.latent.reg_0.1.7.tar.gz |
| Windows binaries: | r-devel: mult.latent.reg_0.1.7.zip, r-release: mult.latent.reg_0.1.7.zip, r-oldrel: mult.latent.reg_0.1.7.zip |
| macOS binaries: | r-release (arm64): mult.latent.reg_0.1.7.tgz, r-oldrel (arm64): mult.latent.reg_0.1.7.tgz, r-release (x86_64): mult.latent.reg_0.1.7.tgz, r-oldrel (x86_64): mult.latent.reg_0.1.7.tgz |
| Old sources: | mult.latent.reg archive |
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