Diederick Vermetten [cre, aut], Furong Ye [cre, aut], Hao Wang [aut], Carola Doerr [aut], Thomas Bäck [aut]
The benchmarking module for the Iterative Optimization Heuristics
Profiler ('IOHprofiler'). This module provides benchmarking in the 'IOHprofiler'
format, which can be visualized using the 'IOHanalyzer' module.

Iterated Ordinary Least Squares Regression
c(person("Nassim", "Zbalah", email = "nas66.nz@gmail.com", role = "cre"), person("David", "Benatia", email = "david.benatia@hec.com", role = "aut"))
Addresses the 'log of zero' by developing a new family of
estimators called iterated Ordinary Least Squares.
This family nests standard approaches such as log-linear and
Poisson regressions, offers several computational advantages,
and corresponds to the correct way to perform the popular
log(Y + 1) transformation. For more details about how to use it,
see the notebook at: <https://www.davidbenatia.com/>.
stats, utils, sandwich, matlib, boot, randomcoloR, stringr
Nassim Zbalah [cre],
David Benatia [aut]

person(
given = "Thomas"
, family = "Soubiran"
, email = "thomas.soubiran@univ-lille.fr"
, role = c("aut", "cre"))
Provides IPv4 and IPv6 support for IP operations such as comparison, arithmetic, bit manipulation and lookup. Both IPv4 and IPv6 arbitrary ranges are also supported as well as domain lookup and whois query.

Iterative Pruning to Capture Population Structure
c(person(given = "Kridsadakorn", family = "Chaichoompu", email = "kridsadakorn@biostatgen.org", role = c("aut", "cre")), person(given = "Kristel", family = 'Van Steen', role = "aut"), person(given = "Fentaw", family = "Abegaz", role = "aut"), person(given = "Sissades", family = "Tongsima", role = "aut"), person(given = "Philip", family = "Shaw", role = "aut"), person(given = "Anavaj", family = "Sakuntabhai", role = "aut"), person(given = "Luisa", family = "Pereira", role = "aut"))
An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) <doi:10.1186/1471-2105-10-382>. The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) <doi:10.18637/jss.v067.i06>. In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'.
'parallelization.R' 'check.stopping.R' 'clustering.mode.R'
'clustering.R' 'data.R' 'export.groups.R' 'get.node.info.R'
'ipcaps-package.R' 'process.each.node.R' 'output.template.R'
'save.html.R' 'save.eigenplots.html.R'
'save.plots.label.html.R' 'save.plots.cluster.html.R'
'save.plots.R' 'postprocess.R' 'preprocess.R' 'ipcaps.R'
'top.discriminator.R'
Kridsadakorn Chaichoompu [aut, cre],
Kristel Van Steen [aut],
Fentaw Abegaz [aut],
Sissades Tongsima [aut],
Philip Shaw [aut],
Anavaj Sakuntabhai [aut],
Luisa Pereira [aut]