Multivariate Imputation by Chained Equations

an R package for structural equation modeling and more

Tidy data structures, summaries, and visualisations for missing data

R package to accompany Time Series Analysis and Its Applications: With R Examples -and- Time Series: A Data Analysis Approach Using R

CRAN R Package: Time Series Missing Value Imputation

An R package for Bayesian structural equation modeling

Factor-Based Imputation for Missing Data

miceRanger: Fast Imputation with Random Forests in R

Tools for multiple imputation in multilevel modeling

missCompare R package - intuitive missing data imputation framework

mde: Missing Data Explorer

Joint Analysis and Imputation of generalized linear models and linear mixed models with missing values

Multiple Imputation with GAMLSS

Iterative Least Square Estimation or Full Information Maximum Likelihood Estimation for Linear Regression When Data Include Missing Values.

R package for adaptive correlation and covariance matrix shrinkage.

CRAN R package: Impute missing values based on automated variable selection

Health economic evaluations from individual level data with missing values using a set of pre-defined Bayesian models written in BUGS. A series of parametric models are available to jointly model partially-observed effectiveness and cost outcomes under both ignorable and nonignroable missing data mechanism assumptions

Inference in Bayesian Networks with R

Extensions and extras for tidy processing.