https://djkt604.r-universe.devRecent package updates in djkt604R-universehttps://github.com/djkt604.pnghttps://djkt604.r-universe.devTue, 28 Apr 2026 12:30:48 GMTkatherine.pollard@gladstone.ucsf.edu (Katherine S. Pollard)The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering).https://github.com/r-universe/bioc/actions/runs/36543544248Tue, 28 Apr 2026 12:30:48 GMThopach2.73.0successhttps://bioc.r-universe.devhttps://github.com/bioc/hopachhopach.Rnwhopach.pdfhopach2013-11-01 19:52:492013-11-01 19:52:49katherine.pollard@gladstone.ucsf.edu (Katherine S. Pollard)Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments.https://github.com/r-universe/bioc/actions/runs/36541738871Tue, 28 Apr 2026 12:29:57 GMTmulttest2.69.0successhttps://bioc.r-universe.devhttps://github.com/bioc/multtest