growcurves
0.2.4.1Bayesian Semi and Nonparametric Growth Curve Models that Additionally Include Multiple Membership Random Effects
Overview
Employs a non-parametric formulation for by-subject random effect parameters to borrow strength over a constrained number of repeated measurement waves in a fashion that permits multiple effects per subject. One class of models employs a Dirichlet process (DP) prior for the subject random effects and includes an additional set of random effects that utilize a different grouping factor and are mapped back to clients through a multiple membership weight matrix; e.g. treatment(s) exposure or dosage. A second class of models employs a dependent DP (DDP) prior for the subject random effects that directly incorporates the multiple membership pattern.
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People & History
15 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.0.0 released · 2020-04-24
- archivedRemoved from CRAN2019-07-02misuse of package= for PACKAGE= was not corrected despite reminder
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 0.2.4.12016-12-21 · diff ↗
- RR 3.3.0 released · 2016-05-03
- 0.2.4.02015-11-15 · diff ↗
- RR 3.2.0 released · 2015-04-16
- RR 3.1.0 released · 2014-04-10
- 0.2.3.92014-02-23 · diff ↗
- 0.2.3.82014-02-19 · diff ↗
- 0.2.3.72014-01-07 · diff ↗
- RR 3.0.0 released · 2013-04-03
- 0.2.3.62013-03-25 · diff ↗
- 0.2.3.52013-03-25 · diff ↗
Package metadata
- Total releases
- 15
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.2.2
- Bundled data
- 128 KB / 6 files
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