Preprocessing and Modeling Kinase Activity Profiles in PamChip
Data
Pushpike Thalikarathne, Ziv Shkedy and Dan Lin
This package contains the functions that can be used to
analyze the PamChip microarray data. The package is mainly
useful in fitting semi-parametric mixed effects models while
accommodating various types of mean structures and correlation
structures present in Kinase activity profiling experiments.
The package can be used to visualize, cleaning Pam Chip data.
And also it can be used in model fitting, velocity estimating,
and testing the estimated velocities between the groups. The
PamGeneMixed package is built up as a software implementation
for the paper titled The Use of Semi-parametric Mixed Models to
Analyze PamChip Peptide Array Data: an Application to an
Oncology Experiment which is published in Bioinformatics
(2011).
R (>= 2.10), mgcv, gplots, utils, graphics, nlme, methods,
stats, Biobase

Pushpike Thalikarathne, Ziv Shkedy, Dan Lin, Lieven Clement,
and Geert Verbeke

c(person("Arnab", "Maity", email = "arnab.maity@pfizer.com", role = c("aut", "cre")),
person("Antik", "Chakraborty", email = "antik.chakraborty@duke.edu", role = "aut"),
person("Anirban", "Bhattacharya", email = "anirbanb@stat.tamu.edu", role = "aut"),
person("Bani", "K. Mallick", email = "bmallick@stat.tamu.edu", role = "aut"),
person("Veerabhadran", "Baladandayuthapani", email = "veerab@umich.edu", role = "aut"))
Provides function for performing Bayesian survival regression using
Horseshoe prior in the accelerated failure time model with log normal assumption
in order to achieve high dimensional pan-cancer variable selection as developed in
Maity et. al. (2019) <doi:10.1111/biom.13132>.
Arnab Maity [aut, cre],
Antik Chakraborty [aut],
Anirban Bhattacharya [aut],
Bani K. Mallick [aut],
Veerabhadran Baladandayuthapani [aut]

A Semi-Parametric Test for Specifying Functional Form
Toke Emil Panduro <tepp@ifro.ku.dk>, Cathrine Ulla Jensen <cuj@ifro.ku.dk>
A central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an  explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations.  In contrast to the classic power-transformations, the methods in this package allows for  theoretical driven user input and the possibility to compare with a non-parametric transformation.
R (>= 3.1.0), mgcv, RColorBrewer, Formula, lasso2