Frequentist assisted by Bayes (FAB) p-values and confidence
interval construction. See
Hoff (2019) <arXiv:1907.12589>
"Smaller p-values via indirect information",
Hoff and Yu (2019) <doi:10.1214/18-EJS1517>
"Exact adaptive confidence intervals for linear regression coefficients", and
Yu and Hoff (2018) <doi:10.1093/biomet/asy009>
"Adaptive multigroup confidence intervals with constant coverage".

Koulik Khamaru <koulik123@gmail.com>, Rahul Mazumder<rahul.mazumder@gmail.com >
Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.

Scores the FACT and FACIT Family of Patient-Reported Outcome
Measures
c(
person("Ray", "Baser", email="ray.stats@gmail.com", role=c("aut", "cre")),
person("Ayelet", "Greenberg", email="greenberg613@gmail.com", role="ctb"))
Provides functions to score the Functional Assessment of Cancer
Therapy (FACT) and Functional Assessment of Chronic Illness Therapy (FACIT)
family of patient-reported outcome (PRO) measures.  The questionnaires
themselves can be downloaded from www.FACIT.org.  For most of the FACIT
questionnaires, FACIT.org provides scoring syntax for use with commercial
statistical software (SAS and SPSS).  The FACTscorer R package is intended
to serve as a free, reliable alternative for those without access to SAS or
SPSS.  Additionally, it will allow R users to both score and analyze the
FACT and FACIT scales in R, avoiding the time-consuming and and error-prone
process of transferring data back-and-forth between statistical software.
Finally, use of the FACTscorer package will prevent many sources of scoring
error common when using SAS and/or SPSS syntax (e.g., copy-paste errors and
other accidental modifications to the syntax).
Ray Baser [aut, cre],
Ayelet Greenberg [ctb]

Variable selection for supervised classification in high
dimension
Emeline Perthame (Agrocampus Ouest, Rennes, France), Chloe Friguet
(Universite de Bretagne Sud, Vannes, France) and David Causeur (Agrocampus
Ouest, Rennes, France)
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing
supervised classification models and variable selection on dependent
covariates. The classification procedures are combined with a factor
modeling of dependence among covariates. The available procedures are Lasso
regularized logistic model (see Friedman et al. (2010)), sparse linear
discriminant analysis (see Clemmensen et al. (2011)), shrinkage linear and
diagonal discriminant analysis (see M. Ahdesmaki et al. (2010)).