Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.

Jose Gerardo Tamez-Pena, Antonio Martinez-Torteya, Israel Alanis and Jorge Orozco

Fast Robust Estimation of Signals in Heterogeneous Data
c(person(given = "Adam",
family = "Lund",
role = c("aut", "cre", "ctb", "cph"),
email = "adam.lund@math.ku.dk"),
person(given = "Benjamin",
family = "Stephens",
role = c("ctb", "cph")),
person(given = "Gael",
family = "Guennebaud",
role = c("ctb", "cph")),
person(given = "Angelo",
family = "Furfaro",
role = c("ctb", "cph")),
person(given = "Luca",
family = "Di Gaspero",
role = c("ctb", "cph")),
person(given = "Brandon",
family = "Whitcher",
role = c("ctb", "cph")))
Procedure for solving the maximin problem for identical design across heterogeneous data groups. Particularly efficient when the design matrix is either orthogonal or has tensor structure. Orthogonal wavelets can be specified for 1d, 2d or 3d data simply by name. For tensor structured design the tensor components (two or three) may be supplied. The package also provides an efficient implementation of the generic magging estimator.
Adam Lund [aut, cre, ctb, cph],
Benjamin Stephens [ctb, cph],
Gael Guennebaud [ctb, cph],
Angelo Furfaro [ctb, cph],
Luca Di Gaspero [ctb, cph],
Brandon Whitcher [ctb, cph]

Post-Processing EddyPro Full Output File
Despike, ustar filtering, plotting, footprint modelling and general post-processing of a standard EddyPro full output file (LI-COR Inc 2011-2015 <https://www.licor.com/env/products/eddy_covariance/eddypro.html>).
lubridate, ggplot2, raster, dismo, sp, RColorBrewer

Relative Importance of Main and Interaction Effects
person(given = "Omar Maghawry", family = "Ibrahim", role = c("aut","cre"),
email = "dromarnrc@gmail.com",
comment = c(ORCID = "orcid.org/0000-0003-3439-9155"))
Computes relative importance of main and interaction effects. Also, sum of the modified generalized weights is computed. Ibrahim et al. (2022) <doi:10.1134/S1064229322080051>.
neuralnet, RSNNS, stats, ggplot2, forcats
Omar Maghawry Ibrahim [aut, cre]
(<https://orcid.org/0000-0003-3439-9155>)