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growcurves (version 0.2.4.0)

Bayesian Semi and Nonparametric Growth Curve Models that Additionally Include Multiple Membership Random Effects

Description

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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Version

Install

install.packages('growcurves')

Monthly Downloads

30

Version

0.2.4.0

License

GPL (>= 2)

Maintainer

Terrance Savitsky

Last Published

November 15th, 2015

Functions in growcurves (0.2.4.0)

XZcov

generate fixed and random design matrices, X and Z
mmmultPost

Bayesian mixed effects model with a DP prior on by-subject effects and more than one multiple membership random effects term
plot.dpgrow

Produce model plots
mmCplusDpPost

Bayesian mixed effects model with a DP prior on by-subject effects and CAR prior on a set of multiple membership effects
summary.dpgrowmm

S3 functions of dpgrowmm
summary.dpgrow

S3 functions of dpgrow
datsimmult

Repeated measures for two groups of subjects with two multiple membership (MM) terms
samples

Produce MCMC samples for model parameters
dateduc

Student test scores and associated teachers for a single school in a large urban school district
effectsplot

Plot comparison of Effect parameters of a Multiple membership (MM) term under varied prior formulations
plot.dpgrowmm

Produce model plots
ddp_quantiles

Produce quantile summaries of model posterior samples
ddpgrow

Bayesian semiparametric growth curve models.
datsim

Repeated measures for two groups of subjects drawn from mmcar model with no nuisance covariates
dpgrow

Bayesian semiparametric growth curve models.
getmf

Produce fixed and random effects design matrices from single formula input
mmIgroupDpPost

Bayesian mixed effects model with a DP prior on by-subject effects and use of group means for multiple membership effects
mmIplusDpPost

Bayesian mixed effects model with a DP prior on by-subject effects and zero mean independent Gaussian priors on multiple membership effects
relabel

Relabel user vector input to sequential numerical
plot.ddpgrow

Produce model plots
samples.dpgrow

Produce samples of MCMC output
dpPost

Run a Bayesian mixed effects model for by-subject random effects with DP prior
dpgrowmm

Bayesian semiparametric growth curve models with employment of multiple membership random effects.
lgmPost

Run a Bayesian mixed effects model for by-subject random effects with an independent Gaussian prior
samples.ddpgrow

Produce samples of MCMC output
summary.ddpgrow

S3 functions of dpgrow
ddpMCMCplots

generate plots of posterior samples under ddpgrow model
growplot

Plot by-subject and by-group growth curves
samples.dpgrowmult

Produce samples of MCMC output
plot.dpgrowmult

Produce model plots
mmCmvplusDpPost

Bayesian mixed effects model with a DP prior on by-subject effects and CAR prior on a multivariate set of multiple membership effects
trtplot

Plot comparison of Mean Effects for Any Two Treatments
datbrghtmodterms

BRIGHT BDI depressive symptom data with (G = 4) module groups divided into separate MM terms.
datsimcov

Repeated measures for two groups of subjects drawn from mmcar model with 2 nuisance covariates
ddpPost

Run a Bayesian mixed effects model for by-subject random effects with DDP prior
ddpEffectsplot

Plot by-subject and by-treatment posterior mean values for dossage random effects
datbrghtterms

BRIGHT BDI depressive symptom data with (G = 4) session groups divided into separate MM terms.
mcmcPlots

generate plots of model(s) posterior results
growcurves-package

Bayesian Semi and Nonparametric Growth Curve Models with Employment of Multiple Membership Random Effects for Longitudinal Data
summary.dpgrowmult

S3 functions of dpgrowmult
summary_quantiles

Produce quantile summaries of model posterior samples
growthCurve

Within subject model-predicted growth curve
dpgrowmult

Bayesian semiparametric growth curve models under employment of more-than-1 multiple membership random effects (block) term.