Methods and Examples from Introduction to the Theory of Observational Studies
Add a Caliper to an Existing Cost Matrix
Add an Integer Penalty to an Existing Distance Matrix
Rank-Based Mahalanobis Distance Matrix
Add a Near-exact Penalty to an Exisiting Distance Matrix.
Cut a Covariate at Quantiles and Add a Penalty for Different Quantile ...
Amplification of sensitivity analysis in observational studies.
Computes individual and pairwise treatment assignment probabilities.
Computes the null expectation and variance for one stratum.
Evaluate Covariate Balance in a Matched Sample
Compute expectations and variances for one stratum.
Convolution of Two Probability Generating Functions
tools:::Rd_package_title("iTOS")
Two-Criteria Matching
Make the Network Used for Matching with Two Criteria
Sensitivity Analysis Using Noether's Test for Matched Pairs
Initialize a Distance Matrix.
zeta function in sensitivity analysis
Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are 'aHDL' from Rosenbaum (2023a) <doi:10.1111/biom.13558> and 'bingeM' from Rosenbaum (2023b) <doi:10.1111/biom.13921>. The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data 'bingeM' from 'binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>). The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'.