c(person("James", "Dai", role = c("aut", "cre"),
email = "jdai@fredhutch.org"),
person("Xiaoyu", "Wang", role = c("aut")))
Several tests for differential methylation in methylation array data, including one-sided differential mean and variance test. Methods used in the package refer to Dai, J, Wang, X, Chen, H and others (2021) "Incorporating increased variability in discovering cancer methylation markers", Biostatistics, submitted.

Functions and data for "Data Mining with R"
This package includes functions and data accompanying the
book "Data Mining with R, learning with case studies" by Luis
Torgo, CRC Press 2010.

This package includes functions and data accompanying the book
"Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.

Functions and Data for the Second Edition of "Data Mining with
R"
Functions and data accompanying the second edition of the book  "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.
person("Luis", "Torgo", email = "ltorgo@dcc.fc.up.pt", role = c("aut", "cre"))

Methods for studying forensic DNA profiling, in particular familial database searches.

Faisal Ababneh, John Robinson, Lars S Jermiin and Hasinur Rahaman Khan
Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and  often impose further restrictions on the parameters.

Tools for analysing forensic genetic DNA data
Functions for making a database comparison exercise where
each DNA profile is compared to any other in the database. Also
has functions for optimising parameters of the population
genetic model and for simulating DNA databases.

Tools for Analysing Forensic Genetic DNA Data
Torben Tvedebrink <tvede@math.aau.dk> and James Curran <j.curran@auckland.ac.nz>. Mikkel Meyer Andersen <mikl@math.aau.dk> assisted on the multicore/CPU implementation.
Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic
is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also
called F-designation) is implemented. Dedicated functions ease plotting the results.