Package: multilevelPSA 1.3.1

multilevelPSA: Multilevel Propensity Score Analysis

Conducts and visualizes propensity score analysis for multilevel, or clustered data. Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.

Authors:Jason Bryer [aut, cre]

multilevelPSA_1.3.1.tar.gz
multilevelPSA_1.3.1.zip(r-4.7)multilevelPSA_1.3.1.zip(r-4.6)multilevelPSA_1.3.1.zip(r-4.5)
multilevelPSA_1.3.1.tgz(r-4.6-any)multilevelPSA_1.3.1.tgz(r-4.5-any)
multilevelPSA_1.3.1.tar.gz(r-4.7-any)multilevelPSA_1.3.1.tar.gz(r-4.6-any)
multilevelPSA_1.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
multilevelPSA/json (API)
NEWS

# Install 'multilevelPSA' in R:

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

Bug tracker:https://github.com/jbryer/multilevelpsa/issues

Pkgdown/docs site:https://jbryer.github.io

Datasets:

pisa.colnames - Mapping of variables in 'pisana' with full descriptions.
pisa.countries - Data frame mapping PISA countries to their three letter abbreviation.
pisa.psa.cols - Character vector representing the list of covariates used for estimating propensity scores.
pisana - North American (i.e. Canada, Mexico, and United States) student results of the 2009 Programme of International Student Assessment.

On CRAN:

6.18 score 16 stars 94 scripts 255 downloads 16 exports 43 dependencies

Last updated from:2b197303c2. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	182
source / vignettes	OK	244
linux-release-x86_64	OK	178
macos-release-arm64	OK	146
macos-oldrel-arm64	OK	137
windows-devel	OK	146
windows-release	OK	155
windows-oldrel	OK	141
wasm-release	OK	126

Exports:covariate.balance difftable.plot getPropensityScores getStrata is.mlpsa loess.plot lsos missing.plot mlpsa mlpsa.circ.plot mlpsa.ctree mlpsa.difference.plot mlpsa.distribution.plot mlpsa.logistic psrange tree.plot

Dependencies:cli codetools coin cpp11 farver ggplot2 glue GPArotation gtable isoband labeling lattice libcoin lifecycle MASS Matrix matrixStats mnormt modeltools multcomp mvtnorm nlme party plyr PSAgraphics psych R6 RColorBrewer Rcpp reshape rlang rpart S7 sandwich scales strucchange survival TH.data vctrs viridisLite withr xtable zoo

Multilevel Propensity Score Analysis

Rendered frommultilevelPSA.Rmdusingknitr::rmarkdownon May 16 2026.

Last update: 2025-04-03
Started: 2025-04-03

Shrinking Fitted Values from Logistic Regression

Rendered fromshrinking_fitted_values.Rmdusingknitr::rmarkdownon May 16 2026.

Last update: 2025-11-17
Started: 2025-11-17

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Multilevel Propensity Score Analysis	multilevelPSA-package multilevelPSA
Adapted from ggExtra package which is no longer available. This is related to an experimental mlpsa plot that will combine the circular plot along with the two individual distributions.	align.plots
Returns the overall effects as a data frame.	as.data.frame.covariate.balance
Estimate covariate effect sizes before and after propensity score adjustment.	covariate.balance
Calculate covariate effect size differences before and after stratification.	covariateBalance
Transformation of Factors to Individual Levels	cv.trans.psa
This function produces a ggplot2 figure containing the mean differences for each level two, or cluster.	difftable.plot
Returns a data frame with two columns corresponding to the level 2 variable and the fitted value from the logistic regression.	getPropensityScores
Returns a data frame with two columns corresponding to the level 2 variable and the leaves from the conditional inference trees.	getStrata
Returns true if the object is of type 'mlpsa'	is.mlpsa
Loess plot with density distributions for propensity scores and outcomes on top and right, respectively.	loess.plot
Nicer list of objects in memory. Particularly useful for analysis of large data. https://stackoverflow.com/questions/1358003/tricks-to-manage-the-available-memory-in-an-r-session	lsos
Returns a heat map graphic representing missingness of variables grouped by the given grouping vector.	missing.plot
This function will perform phase II of the multilevel propensity score analysis.	mlpsa
Plots the results of a multilevel propensity score model.	mlpsa.circ.plot
Estimates propensity scores using the recursive partitioning in a conditional inference framework.	mlpsa.ctree
Creates a graphic summarizing the differences between treatment and comparison groups within and across level two clusters.	mlpsa.difference.plot
Plots distribution for either the treatment or comparison group.	mlpsa.distribution.plot
Estimates propensity scores using logistic regression.	mlpsa.logistic
Mapping of variables in `pisana` with full descriptions.	pisa.colnames
Data frame mapping PISA countries to their three letter abbreviation.	pisa.countries
Character vector representing the list of covariates used for estimating propensity scores.	pisa.psa.cols
North American (i.e. Canada, Mexico, and United States) student results of the 2009 Programme of International Student Assessment.	pisana
Multiple covariate balance assessment plot.	plot.covariate.balance
Plots the results of a multilevel propensity score model.	plot.mlpsa
Plots densities and ranges for the propensity scores.	plot.psrange
Prints the overall effects before and after propensity score adjustment.	print.covariate.balance
Prints basic information about a 'mlpsa' class.	print.mlpsa
Prints information about a psrange result.	print.psrange
Prints the results of [mlpsa()] and [xtable.mlpsa()].	print.xmlpsa
Estimates models with increasing number of comparison subjects starting from 1:1 to using all available comparison group subjects.	psrange
Provides a summary of a 'mlpsa' class.	summary.mlpsa
Prints the summary results of psrange.	summary.psrange
Heat map representing variables used in a conditional inference tree across level 2 variables.	tree.plot
Prints the results of [mlpsa()] as a LaTeX table.	xtable.mlpsa