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growcurves (version 0.2.3.8)

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

0.2.3.8

License

GPL (>= 2)

Maintainer

Terrance Savitsky

Last Published

February 20th, 2014

Functions in growcurves (0.2.3.8)

dateduc

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

Bayesian mixed effects model with a DP prior on by-subject effects and CAR prior on a multivariate set of 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
summary.ddpgrow

S3 functions of dpgrow
samples.dpgrowmult

Produce samples of MCMC output
datbrghtterms

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

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

Produce quantile summaries of model posterior samples
ddpMCMCplots

generate plots of posterior samples under ddpgrow model
getmf

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

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

Relabel user vector input to sequential numerical
ddpPost

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

Bayesian semiparametric growth curve models.
plot.dpgrowmm

Produce model plots
mmIgroupDpPost

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

Produce model plots
dpgrow

Bayesian semiparametric growth curve models.
growthCurve

Within subject model-predicted growth curve
dpgrowmm

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

Produce samples of MCMC output
XZcov

generate fixed and random design matrices, X and Z
datbrghtmodterms

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

Bayesian semi and nonparametric growth curve models with employment of multiple membership random effects for longitudinal data.
ddpEffectsplot

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

Produce quantile summaries of model posterior samples
mmmultPost

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

Plot by-subject and by-group growth curves
effectsplot

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

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

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

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

Plot comparison of Mean Effects for Any Two Treatments
samples

Produce MCMC samples for model parameters
mmCplusDpPost

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

Produce model plots
lgmPost

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

Produce model plots
summary.dpgrowmm

S3 functions of dpgrowmm
summary.dpgrow

S3 functions of dpgrow
samples.dpgrow

Produce samples of MCMC output
mcmcPlots

generate plots of model(s) posterior results
summary.dpgrowmult

S3 functions of dpgrowmult