Package: smcfcs 2.0.0

Jonathan Bartlett

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.

Authors:Jonathan Bartlett [aut, cre], Ruth Keogh [aut], Edouard F. Bonneville [aut], Lars van der Burg [aut], Claus Thorn Ekstrøm [ctb]

smcfcs_2.0.0.tar.gz
smcfcs_2.0.0.tar.gz(r-4.5-noble)smcfcs_2.0.0.tar.gz(r-4.4-noble)
smcfcs_2.0.0.tgz(r-4.4-emscripten)smcfcs_2.0.0.tgz(r-4.3-emscripten)
smcfcs.pdf |smcfcs.html✨
smcfcs/json (API)

# Install 'smcfcs' in R:
install.packages('smcfcs', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/jwb133/smcfcs/issues11 issues

Datasets:
  • ex_cc - Simulated case cohort data
  • ex_coarsening - Simulated example data with a coarsened factor covariate
  • ex_compet - Simulated example data with competing risks outcome and partially observed covariates
  • ex_coxquad - Simulated example data with time to event outcome and quadratic covariate effects
  • ex_dtsam - Simulated discrete time survival data set
  • ex_finegray - Simulated example data with competing risks outcome and partially observed covariates
  • ex_flexsurv - Simulated example data with time-to-event Weibull outcome and two covariates
  • ex_lininter - Simulated example data with continuous outcome and interaction between two partially observed covariates
  • ex_linquad - Simulated example data with continuous outcome and quadratic covariate effects
  • ex_logisticquad - Simulated example data with binary outcome and quadratic covariate effects
  • ex_ncc - Simulated nested case-control data
  • ex_poisson - Simulated example data with count outcome, modelled using Poisson regression

On CRAN:

Conda:

5.41 score 1 packages 1.4k downloads 8 mentions 7 exports 13 dependencies

Last updated 9 days agofrom:561c88dcaa. Checks:2 OK, 1 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 25 2025
R-4.5-linuxOKMar 25 2025
R-4.4-linuxNOTEMar 25 2025

Exports:smcfcssmcfcs.casecohortsmcfcs.dtsamsmcfcs.finegraysmcfcs.flexsurvsmcfcs.nestedccsmcfcs.parallel

Dependencies:abindbackportsbrglm2checkmateenrichwithlatticeMASSMatrixnnetnumDerivrlangsurvivalVGAM

smcfcs for coarsened factor covariates

Jonathan Bartlett and Lars van der Burg

Rendered fromcoarsening.Rmdusingknitr::rmarkdownon Mar 25 2025.

Last update: 2025-03-25
Started: 2025-03-25

smcfcs

Jonathan Bartlett

Rendered fromsmcfcs-vignette.Rmdusingknitr::rmarkdownon Mar 25 2025.

Last update: 2025-03-25
Started: 2015-05-13

smcfcs for covariate measurement error correction

Jonathan Bartlett

Rendered fromcoverror.Rmdusingknitr::rmarkdownon Mar 25 2025.

Last update: 2025-03-25
Started: 2025-03-25

Citation

To cite package ‘smcfcs’ in publications use:

Bartlett J, Keogh R, Bonneville E, van der Burg L (2025). smcfcs: Multiple Imputation of Covariates by Substantive Model Compatible Fully Conditional Specification. R package version 2.0.0, https://CRAN.R-project.org/package=smcfcs.

Corresponding BibTeX entry: 

  @Manual{,
    title = {smcfcs: Multiple Imputation of Covariates by Substantive
      Model Compatible Fully Conditional Specification},
    author = {Jonathan Bartlett and Ruth Keogh and Edouard F.
      Bonneville and Lars {van der Burg}},
    year = {2025},
    note = {R package version 2.0.0},
    url = {https://CRAN.R-project.org/package=smcfcs},
  }

Readme and manuals

smcfcs is an R package implementing Substantive Model Compatibly Fully Conditional Specification Multiple Imputation. Examples and further details are given in the package documentation and vignette.

To install the latest GitHub development version, run:

install.packages("devtools")

devtools::install_github("jwb133/smcfcs")

Help Manual

Help pageTopics
Simulated case cohort dataex_cc
Simulated example data with a coarsened factor covariateex_coarsening
Simulated example data with competing risks outcome and partially observed covariatesex_compet
Simulated example data with time to event outcome and quadratic covariate effectsex_coxquad
Simulated discrete time survival data setex_dtsam
Simulated example data with competing risks outcome and partially observed covariatesex_finegray
Simulated example data with time-to-event Weibull outcome and two covariatesex_flexsurv
Simulated example data with continuous outcome and interaction between two partially observed covariatesex_lininter
Simulated example data with continuous outcome and quadratic covariate effectsex_linquad
Simulated example data with binary outcome and quadratic covariate effectsex_logisticquad
Simulated nested case-control dataex_ncc
Simulated example data with count outcome, modelled using Poisson regressionex_poisson
Assess convergence of a smcfcs objectplot.smcfcs
Substantive model compatible fully conditional specification imputation of covariates.smcfcs
Substantive model compatible fully conditional specification imputation of covariates for case cohort studiessmcfcs.casecohort
Substantive model compatible fully conditional specification imputation of covariates for discrete time survival analysissmcfcs.dtsam
Substantive model compatible fully conditional specification imputation of covariates for a Fine-Gray modelsmcfcs.finegray
Substantive model compatible fully conditional specification imputation of covariates and event times using flexible parametric survival modelssmcfcs.flexsurv
Substantive model compatible fully conditional specification imputation of covariates for nested case control studiessmcfcs.nestedcc
Parallel substantive model compatible imputationsmcfcs.parallel