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JointAI (version 0.1.0)

Joint Analysis and Imputation of Incomplete Data

Description

Provides joint analysis and imputation of linear regression models, generalized linear regression models or linear mixed models with incomplete (covariate) data in the Bayesian framework. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' with the help of the package 'rjags'. It also provides summary and plotting functions for the output.

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Version

Install

install.packages('JointAI')

Monthly Downloads

576

Version

0.1.0

License

GPL (>= 2)

Maintainer

Nicole Erler

Last Published

March 22nd, 2018

Functions in JointAI (0.1.0)

GR_crit

Gelman-Rubin criterion for convergence
JointAI

JointAI: Joint Analysis and Imputation of Missing Values
add_samples

Add samples to an object of class JointAI
JointAIObject

Fitted object of class JointAI
get_MIdat

Extract multiple imputed datasets (and export to SPSS)
MC_error

Monte Carlo error
get_imp_meth

Find default imputation methods and order
check_tvar

Check if a variable is time-varying
default_hyperpars

Get default values for hyperparameters Prints the list of default values for the hyperparameters
densplot

Plot posterior density from JointAI model
predict.JointAI

Predict values from an object of class JointAI
md_pattern

Missing data pattern
model_imp

Joint analysis and imputation of incomplete data
summary.JointAI

Summary of an object of class JointAI
longDF

Longitudinal example dataset
wideDF

Cross-sectional example dataset
traceplot

Traceplot of a JointAI model
predDF

Create a new dataframe for prediction
sim_data

Simulate dataset