Sekhon, J. S. (2011). Multivariate and propensity score matching software with automated balance optimization:
The Matching package for R. Journal of Statistical Software 42(7). http://www.jstatsoft.org/v42/i07
Uses automated procedure to select matches, based on univariate and multivariate balance diagnostics
Primarily 1:M matching (where M is a positive integer), allows matching with or without replacement, caliper, exact
Includes built-in effect and variance estimation procedures
Helmreich, J.E. and Pruzek, R.M. (2009). PSAgraphics: An R Package to Support Propensity Score Analysis. Journal of Statistical Software 29(6).
From webpage: "A collection of functions that primarily produce graphics to aid in a Propensity Score Analysis (PSA). Functions include: cat.psa and box.psa to test balance within strata of categorical and
quantitative covariates, circ.psa for a representation of the estimated effect size by stratum, loess.psa that provides a graphic and loess based effect size estimate, and various balance functions
that provide measures of the balance achieved via a PSA in a categorical covariate."
Abadie, A., Diamond, A., and Hainmueller, H. (2011). Synth: An R Package for Synthetic Control Methods in Comparative Cast Studies. Journal of Statistical
Software 42(13). http://www.jstatsoft.org/v42/i13
Implements weighting approach to creating synthetic control groups
Useful when there is a single treated unit, such as a state or country. Main idea is to form a weighted average of comparison units that, when weighted, looks like
the treated unit.
Nannicini T. (2007). A Simulation-Based Sensitivity Analysis for Matching Estimators. Stata Journal, 7(3), 334-350
Ichino A., Mealli F., Nannicini T. (2008). From Temporary Help Jobs to Permanent Employment: What Can We Learn from Matching Estimators and their
Sensitivity? Journal of Applied Econometrics, 23(3), 305-327.