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 8 days ago

9.00 score 11 stars 1 dependents 59 scripts 1.4k 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 2 years ago

4.78 score 1 dependents 9 scripts 208 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 9 months ago

4.68 score 24 scripts 293 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 1 years ago

4.54 score 7 stars 7 scripts 202 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 7 days ago

3.00 score 1 stars 6 scripts 511 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 2 years ago

2.70 score 10 scripts 208 downloads