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PSAboot (version 1.0)
Bootstrapping for Propensity Score Analysis
Description
Bootstrapping functions for propensity score analysis and matching.
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Version
1.3.6
1.1.4
1.1.3
1.0
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Install
install.packages('PSAboot')
Monthly Downloads
687
Version
1.0
License
GPL
Issues
0
Pull Requests
0
Stars
12
Forks
3
Repository
http://github.com/jbryer/PSAboot
Homepage
http://jason.bryer.org/PSAboot
Maintainer
Jason Bryer
Last Published
November 26th, 2013
Functions in PSAboot (1.0)
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boot.matching
Matching package implementation for bootstrapping.
PSAboot
Bootstraping for propensity score analysis
print.PSAbootSummary
Print method for PSAboot Summary.
pisausa
Programme of International Student Assessment (PISA) results from the United States in 2009.
pisalux
Programme of International Student Assessment (PISA) results from the Luxembourg in 2009.
PSAboot-package
Bootstrapping for Propensity Score Analysis
plot.PSAboot.balance
Plot method for balance.
balance
Returns a summary of the balance for all bootstrap samples.
balance.matchit
Returns balance for each covariate using
matchit
boot.ctree
Stratification using classification trees for bootstrapping.
as.data.frame.PSAbootSummary
Convert the results of PSAboot summary to a data frame.
boot.matchit
MatchIt package implementation for bootstrapping.
hist.PSAboot
Histogram of PSA bootstrap results
pisa.psa.cols
Character vector representing the list of covariates used for estimating propensity scores.
boxplot.PSAboot
Boxplot of PSA boostrap results.
boot.strata
Stratification implementation for bootstrapping.
balance.matching
Returns balance for each covariate using
Match
print.PSAboot
Print results of PSAboot
boxplot.PSAboot.balance
Boxplot of the balance statistics for bootstrapped samples.
boot.rpart
Stratification using classification trees for bootstrapping.
matrixplot
Matrix Plot of Boostrapped Propensity Score Analysis
print.PSAboot.balance
Print method for balance.
summary.PSAboot
Summary of pooled results from PSAboot
plot.PSAboot
Plot the results of PSAboot
psa.strata
Propensity Score Analysis using Stratification