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Rdta: Data Transforming Augmentation for Linear Mixed Models
We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) <doi:10.1080/10618600.2019.1704295>.
Getting started
Browse package contents
Package details |
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| Author | Hyungsuk Tak, Kisung You, Sujit K. Ghosh, and Bingyue Su |
| Maintainer | Hyungsuk Tak <hyungsuk.tak@gmail.com> |
| License | GPL-2 |
| Version | 1.0.1 |
| Package repository | View on CRAN |
| Installation |
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