c(person(given = "Sanja",
family = "Samardzic",
email = "sanja.samardzic@protonmail.com",
role = c("aut")),
person(given = "Julien",
family = "Jacques",
email = "julien.jacques@univ-lyon2.fr",
role = c("cre", "ctb")))
Different regression models with functional features are implemented: for continuous response, categorical response and ordinal response. The model for ordinal response has been published in: 'Analyzing cycling sensors data through ordinal logistic regression with functional covariates, Jacques J. and Samardzic S., Journal of the Royal Statistical Society, Series C, 1-18, 2022'.
Sanja Samardzic [aut],
Julien Jacques [cre, ctb]

Nadezhda M. Belonogova <belon@bionet.nsc.ru> and
Gulnara R. Svishcheva <gulsvi@mail.ru>,
with contributions from:
Seunggeun Lee (kernel functions), Pierre Lafaye de Micheaux ('davies' method),
Thomas Lumley ('kuonen' method), James O. Ramsay (functional data analysis functions),
David Clayton ('read.plink' function) and Brian Ripley ('ginv' function)
Fast regional association analysis of quantitative traits for family-based and population studies.

FREQ: Estimate population size from capture frequencies
Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.

FeatuRE Selection Algorithms for Computer Aided Diagnosis
FRESA.CAD provides a set of functions and feature selection algorithms for building Computer Aided Diagnosis Models
class, cvTools, glmnet, speedglm, gplots, RColorBrewer, nlme,
rpart

Jose Gerardo Tamez-Pena, Antonio Martinez-Torteya and Israel Alanis
Contains a set of utilities for building and testing formula-based models (linear, logistic or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.

Feature Selection Algorithms for Computer Aided Diagnosis