Partitioning Chi-Squared and Tau Index for Three-Way Contingency
Tables

c(
person("Erle", "Holgersen", role = c("aut", "cre"), email = "Erle.Holgersen@icr.ac.uk"),
person("Olivia", "Leavy", role = "aut"),
person("Olivia", "Fletcher", role = "aut"),
person("Frank", "Dudbridge", role = "aut"),
person("Syed", "Haider", role = "aut")
)
Erle Holgersen [aut, cre],
Olivia Leavy [aut],
Olivia Fletcher [aut],
Frank Dudbridge [aut],
Syed Haider [aut]
Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.
data.table, MASS, stats, foreach, doParallel
knitr, rmarkdown, testthat, GenomicInteractions, Gviz

c(
person("Erle", "Holgersen", role = c("aut", "cre"), email = "erle.holgersen@gmail.com"),
person("Olivia", "Leavy", role = "aut"),
person("Olivia", "Fletcher", role = "aut"),
person("Frank", "Dudbridge", role = "aut"),
person("Syed", "Haider", role = "aut")
)
data.table, MASS, stats, foreach, doParallel, iterators
knitr, rmarkdown, testthat, GenomicInteractions, Gviz,
countreg

R (>= 3.5.0), gamlss.tr, gamlss, data.table
MASS, stats, foreach, doParallel, iterators, bedr, knitr,
rmarkdown
testthat, GenomicInteractions, GenomicRanges, Gviz, countreg

c(
person("Erle", "Holgersen", role = "aut"),
person("Olivia", "Leavy", role = "aut"),
person("Olivia", "Fletcher", role = "aut"),
person("Frank", "Dudbridge", role = "aut"),
person("Syed", "Haider", role = c("aut", "cre"), email = "Syed.Haider@icr.ac.uk")
)
Erle Holgersen [aut],
Olivia Leavy [aut],
Olivia Fletcher [aut],
Frank Dudbridge [aut],
Syed Haider [aut, cre]

Routines for efficient cluster analysis of large scale data. The package implements the 'CHICKN'
clustering algorithm, which clusters the data into K groups by relying on the compressed data version.
The package provides functions for data compression, hierarchical clustering and result post processing.
The used data compression procedure is based on the sketching learning N. 'Keriven', N. 'Tremblay', Y. 'Traonmilin',
R. 'Gribonval' (2017) <arXiv:1606.02838v2> and 'Nystrom' kernel approximation S. Wang, A. 'Gittens',
M. W. 'Mahoney' (2019) <arXiv:1706.02803v4>. Hierarchical clustering relies on the 'Compressive' K means algorithm
N. 'Keriven', A. 'Bourrier', R. 'Gribonval', P. Perez (2017) <arXiv:1610.08738v4>.
Rcpp, bigstatsr (>= 1.2.3), RcppParallel, mvnfast, zipfR,
MASS, pracma, nloptr, foreach, doRNG, parallel, doParallel,
Rdpack
Rcpp, RcppArmadillo, RcppParallel, bigstatsr (>= 1.2.3),
rmio