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.
BSD_2_clause + file LICENSE | GPL (>= 2)

An unsupervised non hierarchical clustering algorithm handling
biological data

Universite Pierre et Marie Curie, Centre National de la
Recherche Scientifique

Exact Confidence Limits after a Sequential Trial
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>.

Constrained Inference for Linear Mixed Effects Models.
Implements constrained inference for linear mixed effects models using residual bootstrap methodology.

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

MASS, nlme, methods, isotone, stringr, prettyR, stats,
openxlsx, graphics
Estimation and inference for linear models where some or all of the
fixed-effects coefficients are subject to order restrictions. This package uses
the robust residual bootstrap methodology for inference, and can handle some
structure in the residual variance matrix.
c(person("Casey M."  , "Jelsema", email="jelsema.casey@gmail.com", role=c("aut", "cre") ),
person("Shyamal D.", "Peddada", role="aut" ))
Casey M. Jelsema [aut, cre],
Shyamal D. Peddada [aut]