Package: gFormulaMI 1.0.1
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.
Authors:
gFormulaMI_1.0.1.tar.gz
gFormulaMI_1.0.1.zip(r-4.5)gFormulaMI_1.0.1.zip(r-4.4)gFormulaMI_1.0.1.zip(r-4.3)
gFormulaMI_1.0.1.tgz(r-4.4-any)gFormulaMI_1.0.1.tgz(r-4.3-any)
gFormulaMI_1.0.1.tar.gz(r-4.5-noble)gFormulaMI_1.0.1.tar.gz(r-4.4-noble)
gFormulaMI_1.0.1.tgz(r-4.4-emscripten)gFormulaMI_1.0.1.tgz(r-4.3-emscripten)
gFormulaMI.pdf |gFormulaMI.html✨
gFormulaMI/json (API)
# Install 'gFormulaMI' in R: |
install.packages('gFormulaMI', repos = c('https://jwb133.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jwb133/gformulami/issues
- simDataFullyObs - Simulated fully observed data frame
Last updated 10 months agofrom:82d8ca6c05. Checks:OK: 1 WARNING: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 03 2024 |
R-4.5-win | WARNING | Nov 03 2024 |
R-4.5-linux | WARNING | Nov 03 2024 |
R-4.4-win | WARNING | Nov 03 2024 |
R-4.4-mac | WARNING | Nov 03 2024 |
R-4.3-win | WARNING | Nov 03 2024 |
R-4.3-mac | WARNING | Nov 03 2024 |
Exports:gFormulaImputesyntheticPool
Dependencies:backportsbitbit64bootbroomclicliprcodetoolscpp11crayondplyrfansiforcatsforeachgenericsglmnetgluehavenhmsiteratorsjomolatticelifecyclelme4magrittrMASSMatrixmiceminqamitmlnlmenloptrnnetnumDerivordinalpanpillarpkgconfigprettyunitsprogresspurrrR6RcppRcppEigenreadrrlangrpartshapestringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsvroomwithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
G-formula multiple imputation | gFormulaImpute |
Simulated fully observed data frame | simDataFullyObs |
Pool estimates and variances obtained by analysing multiple synthetic datasets | syntheticPool |