Title: Benchmarking Part of the 'IOHprofiler'
person("Furong", "Ye", email = "f.ye@liacs.leidenuniv.nl", role = "aut"),
person("Hao", "Wang", email = "h.wang@liacs.leidenuniv.nl", role = "aut"),
person("Diederick", "Vermetten", email="d.l.vermetten@liacs.leidenuniv.nl", role = c("aut","cre")),
Description: The benchmarking module for the Iterative Optimization Heuristics
Profiler ('IOHprofiler'). This module provides benchmarking in the 'IOHprofiler'
format. It enables performance tracking and analysis for any iterative optimization heuristic,
which can be visualized and analyzed using the 'IOHanalyzer' package. It also provides access
to predefined benchmarking functions, both for pseudo-boolean and continuous domains, as well
Imports: Rcpp (>= 1.0.1), assertthat, magrittr

Author: Diederick Vermetten [cre, aut], Furong Ye [cre, aut], Hao Wang [aut], Carola Doerr [aut], Thomas Bäck [aut]
format, which can be visualized using the 'IOHanalyzer' module.

Title: Iterated Ordinary Least Squares Regression
Authors@R: c(person("Nassim", "Zbalah", email = "nas66.nz@gmail.com", role = "cre"), person("David", "Benatia", email = "david.benatia@hec.com", role = "aut"))
Description: 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/>.
Imports: stats, utils, sandwich, matlib, boot, randomcoloR, stringr

Title: Classes and Methods for IP Addresses
Description: 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.

Title: Iterative Pruning to Capture Population Structure
Authors@R: 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"))
Description: 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'.
Packaged: 2018-06-11 13:05:39 UTC; kridsadakorn