Description: CLAG (for CLusters AGgregation) is an unsupervised non hierarchical clustering algorithm designed to cluster a large variety of biological data and to provide a clustered matrix and numerical values indicating cluster strength. CLAG clusterizes correlation matrices for residues in protein families, gene-expression and miRNA data related to various cancer types, sets of species described by multidimensional vectors of characters, binary matrices. It does not ask to all data points to cluster and it converges yielding the same result at each run. Its simplicity and speed allows it to run on reasonably large datasets.
License: BSD_2_clause + file LICENSE | GPL (>= 2)

Title: An unsupervised non hierarchical clustering algorithm handling

Copyright: Universite Pierre et Marie Curie, Centre National de la

Title: Exact Confidence Limits after a Sequential Trial
Maintainer: Chris J. Lloyd <c.lloyd@mbs.edu>
Description: The user first provides design vectors n, a and b as well as null (p0) and alternative (p1) benchmark values for the probability of success. The key function "mv.plots.SM()" calculates mean values of exact upper and lower limits based on four different rank ordering methods. These plots form the basis of selecting a rank ordering. The function "inference()" calculates exact limits from a provided realisation and ordering choice. For more information, see "Exact confidence limits after a group sequential single arm binary trial" by Lloyd, C.J. (2020), Statistics in Medicine, Volume 38, 2389-2399, <doi:10.1002/sim.8909>.

Title: Constrained Inference for Linear Mixed Effects Models.
Imports: MASS, quadprog, shiny, methods, Iso
Description: Implements constrained inference for linear mixed effects models using residual bootstrap methodology.
Maintainer: Casey M. Jelsema <casey.jelsema@nih.gov>
Packaged: 2014-10-17 16:02:45 UTC; jelsemacm

Title: Constrained Inference for Linear Mixed Effects Models
Imports: MASS, lme4, shiny, methods, isotone, stringr, prettyR, stats
Description: Constrained inference for linear mixed effects models using residual bootstrap methodology