smcfcs - Multiple Imputation of Covariates by Substantive Model Compatible Fully Conditional Specification

Implements multiple imputation of missing covariates by Substantive Model Compatible Fully Conditional Specification. This is a modification of the popular FCS/chained equations multiple imputation approach, and allows imputation of missing covariate values from models which are compatible with the user specified substantive model.

Last updated 7 days ago

8.64 score 11 stars 1 packages 62 scripts 2.2k downloads

InformativeCensoring - Multiple Imputation for Informative Censoring

Multiple Imputation for Informative Censoring. This package implements two methods. Gamma Imputation described in <DOI:10.1002/sim.6274> and Risk Score Imputation described in <DOI:10.1002/sim.3480>.

Last updated 1 years ago

4.78 score 1 packages 9 scripts 249 downloads

dejaVu - Multiple Imputation for Recurrent Events

Performs reference based multiple imputation of recurrent event data based on a negative binomial regression model, as described by Keene et al (2014) <doi:10.1002/pst.1624>.

Last updated 4 months ago

4.68 score 24 scripts 267 downloads

gFormulaMI - G-Formula for Causal Inference via Multiple Imputation

Implements the G-Formula method for causal inference with time-varying treatments and confounders using Bayesian multiple imputation methods, as described by Bartlett, Olarte Parra and Daniel (2023) <arXiv:2301.12026>. It creates multiple synthetic imputed datasets under treatment regimes of interest using the 'mice' package. These can then be analysed using rules developed for analysing multiple synthetic datasets.

Last updated 10 months ago

4.54 score 7 stars 7 scripts 153 downloads

bootImpute - Bootstrap Inference for Multiple Imputation

Bootstraps and imputes incomplete datasets. Then performs inference on estimates obtained from analysing the imputed datasets as proposed by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>.

Last updated 2 months ago

2.70 score 6 scripts 431 downloads

mlmi - Maximum Likelihood Multiple Imputation

Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.

Last updated 1 years ago

2.70 score 10 scripts 224 downloads