Package: propensity 0.1.0.9000

Malcolm Barrett

propensity: A Toolkit for Calculating and Working with Propensity Scores

Calculates propensity score weights for multiple causal 'estimands' across binary, continuous, and categorical exposures. Provides methods for handling extreme propensity scores through trimming, truncation, and calibration. Includes inverse probability weighted estimators that correctly account for propensity score estimation uncertainty.

Authors:Malcolm Barrett [aut, cre, cph]

propensity_0.1.0.9000.tar.gz
propensity_0.1.0.9000.zip(r-4.7)propensity_0.1.0.9000.zip(r-4.6)propensity_0.1.0.9000.zip(r-4.5)
propensity_0.1.0.9000.tgz(r-4.6-any)propensity_0.1.0.9000.tgz(r-4.5-any)
propensity_0.1.0.9000.tar.gz(r-4.7-any)propensity_0.1.0.9000.tar.gz(r-4.6-any)
propensity_0.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
propensity/json (API)

# Install 'propensity' in R:

install.packages('propensity', repos = c('https://r-causal.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/r-causal/propensity/issues

Pkgdown/docs site:https://r-causal.github.io

On CRAN:

7.56 score 23 stars 1 packages 106 scripts 328 downloads 29 exports 7 dependencies

Last updated from:1bd5e02f2c. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	206
source / vignettes	OK	219
linux-release-x86_64	OK	173
macos-release-arm64	OK	94
macos-oldrel-arm64	OK	135
windows-devel	OK	110
windows-release	OK	101
windows-oldrel	OK	106
wasm-release	OK	131

Exports:as_psw estimand estimand<-ipw is_causal_wt is_ps_calibrated is_ps_trimmed is_ps_truncated is_psw is_refit is_stabilized is_unit_trimmed is_unit_truncated new_psw ps_calibrate ps_refit ps_trim ps_trim_meta ps_trunc ps_trunc_meta psw wt_atc wt_ate wt_atm wt_ato wt_att wt_atu wt_cens wt_entropy

Dependencies:cli glue lifecycle rlang tidyselect vctrs withr

Getting Started with propensity

Rendered frompropensity.Rmdusingknitr::rmarkdown

Last update: 2026-02-23
Started: 2026-02-23

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
propensity: A Toolkit for Calculating and Working with Propensity Scores	propensity-package propensity
Inverse Probability Weighted Estimation	as.data.frame.ipw ipw
Check if propensity scores are calibrated	is_ps_calibrated
Test whether propensity scores have been trimmed	is_ps_trimmed
Test whether propensity scores have been truncated	is_ps_truncated
Check if propensity scores have been refit	is_refit
Identify which units were trimmed	is_unit_trimmed
Identify which units were truncated	is_unit_truncated
Calibrate propensity scores	ps_calibrate
Refit a Propensity Score Model on Retained Observations	ps_refit
Trim Propensity Scores	ps_trim
Extract trimming metadata from a 'ps_trim' object	ps_trim_meta
Truncate (Winsorize) Propensity Scores	ps_trunc
Extract truncation metadata from a 'ps_trunc' object	ps_trunc_meta
Propensity Score Weight Vectors	as_psw estimand estimand<- is_causal_wt is_psw is_stabilized new_psw psw
Calculate propensity score weights	wt_atc wt_ate wt_ate.data.frame wt_atm wt_atm.data.frame wt_ato wt_ato.data.frame wt_att wt_att.data.frame wt_atu wt_atu.data.frame wt_cens wt_cens.data.frame wt_entropy wt_entropy.data.frame