# Bayesian semiparametric growth curve models that additionally include multiple membership random effects.

## Documentation for package ‘growcurves’ version 0.1.1

- DESCRIPTION file.
- Package NEWS.

## Help Pages

growcurves-package | Bayesian semiparametric growth curve models with employment of multiple membership random effects.
datbrghtterms | BRIGHT BDI depressive symptom data with (G = 4) session groups divided into separate MM terms.
datsim | Repeated measures for two groups of subjects drawn from mmcar model with no nuisance covariates
datsimcov | Repeated measures for two groups of subjects drawn from mmcar model with 2 nuisance covariates
datsimmult | Repeated measures for two groups of subjects with two multiple membership (MM) terms
dpgrow | Bayesian semiparametric growth curve models.
dpgrow.default | Bayesian semiparametric growth curve models.
dpgrowmm | Bayesian semiparametric growth curve models with employment of multiple membership random effects.
dpgrowmm.default | Bayesian semiparametric growth curve models with employment of multiple membership random effects.
dpgrowmult | Bayesian semiparametric growth curve models under employment of more-than-'1' multiple membership random effects (block) term.
dpgrowmult.default | Bayesian semiparametric growth curve models under employment of more-than-'1' multiple membership random effects (block) term.
dpPost | Run a Bayesian mixed effects model for by-subject random effects with DP prior
effectsplot | Plot comparison of Effect parameters of a Multiple membership (MM) term under varied prior formulations
get.mf | Produce fixed and random effects design matrices from single formula input
getmf | Produce fixed and random effects design matrices from single formula input
growcurves | Bayesian semiparametric growth curve models with employment of multiple membership random effects.
growplot | Plot by-subject and by-group growth curves
growthCurve | Within subject model-predicted growth curve
lgmPost | Run a Bayesian mixed effects model for by-subject random effects with an independent Gaussian prior
mcmcPlots | generate plots of model(s) posterior results
mmC | Bayesian mixed effects model with a DP prior on by-subject effects and CAR prior on a set of multiple membership effects
mmCplusDpPost | Bayesian mixed effects model with a DP prior on by-subject effects and CAR prior on a set of multiple membership effects
mmI | Bayesian mixed effects model with a DP prior on by-subject effects and zero mean independent Gaussian priors on multiple membership effects
mmIgroup | Bayesian mixed effects model with a DP prior on by-subject effects and use of group means for multiple membership effects
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
mmmultPost | Bayesian mixed effects model with a DP prior on by-subject effects and more than one multiple membership random effects term
package-growcurves | Bayesian semiparametric growth curve models with employment of multiple membership random effects.
plot.dpgrow | Produce model plots
plot.dpgrowmm | Produce model plots
plot.dpgrowmult | Produce model plots
plotmcmc | generate plots of model(s) posterior results
quantiles | Produce quantile summaries of model posterior samples
relabel | Relabel user vector input to sequential numerical
samples | Produce MCMC samples for model parameters
samples.dpgrow | Produce samples of MCMC output
samples.dpgrowmm | Produce MCMC samples for model parameters
samples.dpgrowmult | Produce samples of MCMC output
summary.dpgrow | S3 functions of dpgrow
summary.dpgrowmm | S3 functions of dpgrowmm
summary.dpgrowmult | S3 functions of dpgrowmult
summary_quantiles | Produce quantile summaries of model posterior samples
trtplot | Plot comparison of Mean Effects for Any Two Treatments
XZ | generate fixed and random design matrices, X and Z
XZcov | generate fixed and random design matrices, X and Z