Title: Luck-Corrected Peer Performance Analysis in R
Depends: snow, sandwich, lmtest, compiler
Description: Provides functions to perform the peer performance
analysis of funds' returns as described in Ardia and Boudt (2016) <doi:10.2139/ssrn.2000901>.

Depends: parallel, sandwich, lmtest, compiler
analysis of funds' returns as described in Ardia and Boudt (2018) <doi:10.1016/j.jbankfin.2017.10.014>.

Authors@R: c(person("David", "Ardia", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2823-782X"),
person("Nabil", "Bouamara", role = "ctb"))
Author: David Ardia [aut, cre] (<https://orcid.org/0000-0003-2823-782X>),

Title: Regularization in Cox Frailty Models
Description: A regularization approach for Cox Frailty Models by penalization methods is provided.

Authors@R: c(person("Yan", "Liu", email = "yanliuresearch@gmail.com",
Description: Currently incorporate the generalized odds-rate model (a type of linear
transformation model) for interval-censored data based on penalized monotonic B-Spline.
More methods under other semiparametric models such as cure model or additive model will
be included in future versions. For more details see Lu, M., Liu, Y., Li, C. and Sun, J.
Imports: stats, numDeriv, splines, Matrix, MASS

Title: Group l1 penalized logistic normal multinomial (LNM) regression
Description: The package is developed for the cross-sectional study of
diet and stool microbiome data. The model facility variable
selection. The group l1 penalized logistic normal (LN) approach

Title: Insilico peptide mutation, digestion and homologous comparison.
Description: Amino acid exchange based on single nucleotide variant (SNV) information and tryptic digestion on peptide sequence. Searching for homologous by comparison of tryptic digested peptide sequences.

person("Pritchard", "David", email="dpritch@live.unc.edu", role=c("aut", "cre")),
Description: An implementation of the data processing and data analysis portion
of a pipeline named the PepSAVI-MS which is currently under development by
the Hicks laboratory at the University of North Carolina.  The statistical
analysis package presented herein provides a collection of software tools
used to facilitate the prioritization of putative bioactive peptides from a
complex biological matrix.  Tools are provided to deconvolute mass
spectrometry features into a single representation for each peptide charge
state, filter compounds to include only those possibly contributing to the
observed bioactivity, and prioritize these remaining compounds for those
most likely contributing to each bioactivity data set.