Package: lmw 0.0.2

lmw: Linear Model Weights

Computes the implied weights of linear regression models for estimating average causal effects and provides diagnostics based on these weights. These diagnostics rely on the analyses in Chattopadhyay and Zubizarreta (2023) <doi:10.1093/biomet/asac058> where several regression estimators are represented as weighting estimators, in connection to inverse probability weighting. 'lmw' provides tools to diagnose representativeness, balance, extrapolation, and influence for these models, clarifying the target population of inference. Tools are also available to simplify estimating treatment effects for specific target populations of interest.

Authors:Ambarish Chattopadhyay [aut], Noah Greifer [aut, cre], Jose Zubizarreta [aut]

lmw_0.0.2.tar.gz
lmw_0.0.2.zip(r-4.5)lmw_0.0.2.zip(r-4.4)lmw_0.0.2.zip(r-4.3)
lmw_0.0.2.tgz(r-4.4-any)lmw_0.0.2.tgz(r-4.3-any)
lmw_0.0.2.tar.gz(r-4.5-noble)lmw_0.0.2.tar.gz(r-4.4-noble)
lmw_0.0.2.tgz(r-4.4-emscripten)lmw_0.0.2.tgz(r-4.3-emscripten)
lmw.pdf |lmw.html
lmw/json (API)
NEWS

# Install 'lmw' in R:
install.packages('lmw', repos = c('https://ngreifer.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/ngreifer/lmw/issues

Datasets:
  • lalonde - Data from National Supported Work Demonstration and PSID, as analyzed by Dehejia and Wahba (1999).

On CRAN:

4.47 score 11 stars 18 scripts 171 downloads 3 exports 9 dependencies

Last updated 10 months agofrom:e86a8f967f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-winOKNov 04 2024
R-4.5-linuxOKNov 04 2024
R-4.4-winOKNov 04 2024
R-4.4-macOKNov 04 2024
R-4.3-winOKNov 04 2024
R-4.3-macOKNov 04 2024

Exports:lmwlmw_estlmw_iv

Dependencies:backportschkcligluelatticelifecyclerlangsandwichzoo