Package: CIMTx 1.2.0
CIMTx: Causal Inference for Multiple Treatments with a Binary Outcome
Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. <doi:10.1177/0962280220921909>.
Authors:
CIMTx_1.2.0.tar.gz
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CIMTx.pdf |CIMTx.html✨
CIMTx/json (API)
# Install 'CIMTx' in R: |
install.packages('CIMTx', repos = c('https://jiayiji.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:2bb67b80b8. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 14 2024 |
R-4.5-win | NOTE | Oct 14 2024 |
R-4.5-linux | NOTE | Oct 14 2024 |
R-4.4-win | NOTE | Oct 14 2024 |
R-4.4-mac | NOTE | Oct 14 2024 |
R-4.3-win | NOTE | Oct 14 2024 |
R-4.3-mac | NOTE | Oct 14 2024 |
Exports:ce_estimatedata_simsatrue_c_fun_cal
Dependencies:abindarmbackportsBARTbitopsbootcachemcaToolscheckmatechkclassclassIntclicobaltcodacodetoolscolorspacecowplotcpp11crayoncvAUCdata.tableDBIdeldirdigestdoParalleldplyre1071fansifarverfastmapforeachFormulaformula.toolsgamgbmgenericsggplot2glmnetgluegplotsgridExtragtablegtoolsinterpisobanditeratorsjpegjsonliteKernSmoothlabelinglatticelatticeExtralifecyclelme4lubridatemagrittrMASSMatchingMatrixMatrixModelsmemoisemetRmgcvminqamitoolsmunsellnlmenloptrnnetnnlsnumDerivoperator.toolspillarpkgconfigplyrpngproxypurrrR6RColorBrewerRcppRcppArmadilloRcppEigenrlangROCRs2scalessfshapestringistringrSuperLearnersurveysurvivaltibbletidyrtidyselecttimechangetmletwangunitsutf8vctrsviridisLiteWeightItwithrwkxgboostxtable