# tools:::Rd_package_title("weightedRank") tools:::Rd_package_description("weightedRank") package ## Details The DESCRIPTION file: tools:::Rd_package_DESCRIPTION("weightedRank") tools:::Rd_package_indices("weightedRank") The package conducts either fixed or adaptive sensitivity analyses for observational studies with I blocks and J individuals in each block, one treated and J-1 controls. The two main functions are wgtRank() for a fixed test statistic, and wgtRanktt() for an adaptive choice of one of two test statistics. The function wgtRankCI() inverts the test to obtain confidence intervals and Hodges-Lehmann point estimates. The function ef2C() is used to extract two evidence factors when a treated group is compared to two different control groups. ## Author(s) tools:::Rd_package_author("weightedRank") Maintainer: tools:::Rd_package_maintainer("weightedRank") ## References Berk, R. H. and Jones, D. H. (1978) Relatively optimal combinations of test statistics. Scandinavian Journal of Statistics, 5, 158-162. Quade, D. (1979) Using weighted rankings in the analysis of complete blocks with additive block effects. Journal of the American Statistical Association, 74, 680-683. Rosenbaum, P. R. (1987). The role of a second control group in an observational study. Statistical Science, 2, 292-306. Rosenbaum, P. R. (2011) A new U‐Statistic with superior design sensitivity in matched observational studies. Biometrics, 67(3), 1017-1027. Rosenbaum, P. R. (2012) Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774. Rosenbaum, P. R. (2021) Replication and Evidence Factors in Observational Studies. Chapman and Hall/CRC. Rosenbaum, P. R. (2023) A second evidence factor for a second control group. Biometrics, 79(4), 3968-3980. Rosenbaum, P. R. (2024) Bahadur efficiency of observational block designs. Journal of the American Statistical Association. Tardif, S. (1987) Efficiency and optimality results for tests based on weighted rankings. Journal of the American Statistical Association, 82(398), 637-644. ## Examples ```r data(aHDL) y<-t(matrix(aHDL$hdl,4,406)) wgtRank(y,phi="u878",gamma=6) # New U-statistic weights (8,7,8) wgtRanktt(y,phi1="u868",phi2="u878",gamma=5.9) ```

weightedRank-package