Learn R Programming

mi (version 1.2)

Missing Data Imputation and Model Checking

Description

The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.

Copy Link

Version

Install

install.packages('mi')

Monthly Downloads

19,130

Version

1.2

License

GPL (>= 2)

Maintainer

Ben Goodrich

Last Published

September 2nd, 2025

Functions in mi (1.2)

07complete

Extract the Completed Data
03change

Make Changes to Discretionary Characteristics of Missing Variables
06pool

Estimate a Model Pooling Over the Imputed Datasets
01missing_variable

Class "missing_variable" and Inherited Classes
00mi-package

Iterative Multiple Imputation from Conditional Distributions
04mi

Multiple Imputation
CHAIN

Subset of variables from the CHAIN project
05Rhats

Convergence Diagnostics
allcategorical_missing_data.frame

Class "allcategorical_missing_data.frame"
02missing_data.frame

Class "missing_data.frame"
categorical

Class "categorical" and Inherited Classes
get_parameters

An Extractor Function for Model Parameters
irrelevant

Class "irrelevant" and Inherited Classes
fit_model

Wrappers To Fit a Model
continuous

Class "continuous"
experiment_missing_data.frame

Class "experiment_missing_data.frame"
hist

Histograms of Multiply Imputed Data
count-class

Class "count"
censored-continuous-class

The "censored-continuous" Class, the "truncated-continuous" Class and Inherited Classes
bounded-continuous-class

Class "bounded-continuous"
mipply

Apply a Function to a Object of Class mi
multinomial

The multinomial family
nlsyV

National Longitudinal Survey of Youth Extract
mi-internal

Internal Functions and Methods
mi2stata

Exports completed data in Stata (.dta) or comma-separated (.csv) format
rdata.frame

Generate a random data.frame with tunable characteristics
semi-continuous-class

Class "semi-continuous" and Inherited Classes
multilevel_missing_data.frame

Class "multilevel_missing_data.frame"
positive-continuous-class

Class "positive-continuous" and Inherited Classes