c(person(given = "Stanislav", family = "Zamecnik", email = c("zamecnik@math.muni.cz"), role = c("aut", "cre")),
person(given = "Vojtech", family = "Sindlar", comment = c("<xsindlarv@math.muni.cz>"), role = c("aut")),
person(given = "Zdenka", family = "Gerslova", comment = c("<gerslovaz@math.muni.cz>"), role = c("aut")),
person(given = "Gejza", family = "Wimmer", comment = c("<wimmer@mat.savba.sk>"), role = "aut"),
person(given = "Technology Agency of the Czech Republic", comment = c("<https://www.tacr.cz>"), role = c("fnd")),
person(given = "Masaryk University, a public university, ID: 00216224", role = c("cph")))
Package for estimating the parameters of a nonlinear function using iterated linearization via Taylor series.  Method is based on  Kubáček (2000) ISBN: 80-244-0093-6. The algorithm is a generalization of the procedure given in Köning, R., Wimmer, G.
and Witkovský, V. (2014) <doi:10.1088/0957-0233/25/11/115001>.

c(
person("Tomonari", "Sei", email = "sei@mist.i.u-tokyo.ac.jp", role = "aut"),
person("Masaki", "Hamada", email = "masaki_hamada@mist.i.u-tokyo.ac.jp", role = "cre"))
Consider a data matrix of n individuals with p variates. The objective general index (OGI)
is a general index that combines the p variates into a univariate index in order to rank the n
individuals. The OGI is always positively correlated with each of the variates.
More details can be found in Sei (2016) <doi:10.1016/j.jmva.2016.02.005>.

Ordered Homogeneity Pursuit Lasso for Group Variable Selection
c(
person("You-Wu", "Lin", email = "lyw015813@126.com", role = c("aut")),
person("Nan", "Xiao", email = "me@nanx.me", role = "cre"))
Ordered homogeneity pursuit lasso (OHPL)
algorithm for group variable selection proposed in Lin et al. (2017)
<DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method takes the
homogeneity structure in high-dimensional data into account and
enjoys the grouping effect to select groups of important variables
automatically. This feature makes it particularly useful for
high-dimensional datasets with strongly correlated variables,
such as spectroscopic data.