Package: MLCausal 0.1.0
MLCausal: Causal Inference Methods for Multilevel and Clustered Data
Provides an end-to-end workflow for estimating average treatment effects in clustered (multilevel) observational data. Core functionality includes cluster-aware propensity score estimation using fixed effects and Mundlak-style specifications, inverse probability weighting, within-cluster nearest-neighbor matching, covariate balance diagnostics at both individual and cluster-mean levels, outcome regression with cluster-robust standard errors, propensity score overlap visualization, and tipping-point sensitivity analysis for omitted cluster-level confounding.
Authors:
MLCausal_0.1.0.tar.gz
MLCausal_0.1.0.zip(r-4.7-any)MLCausal_0.1.0.zip(r-4.6-any)MLCausal_0.1.0.zip(r-4.5-any)
MLCausal_0.1.0.tgz(r-4.6-any)MLCausal_0.1.0.tgz(r-4.5-any)
MLCausal_0.1.0.tar.gz(r-4.7-any)MLCausal_0.1.0.tar.gz(r-4.6-any)
MLCausal_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MLCausal/json (API)
| # Install 'MLCausal' in R: |
| install.packages('MLCausal', repos = c('https://subirhait.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/causalfragility-lab/mlcausal/issues
Last updated from:a0632ea1ed. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | NOTE | 132 | ||
| source / vignettes | OK | 181 | ||
| linux-release | NOTE | 136 | ||
| macos-release | NOTE | 145 | ||
| macos-oldrel | NOTE | 130 | ||
| windows-devel | NOTE | 86 | ||
| windows-release | NOTE | 63 | ||
| windows-oldrel | NOTE | 78 | ||
| wasm-release | OK | 112 |
Exports:balance_mlestimate_att_mlml_matchml_psml_weightplot_overlap_mlsens_mlsimulate_ml_data
Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglatticelifecyclelmtestR6RColorBrewerrlangS7sandwichscalesvctrsviridisLitewithrzoo