Title: : Disease-Gene-Variant Relations Mining from the Public
Depends: R (>= 3.0.0), XML, XML2R, curl, stringr

Title: Testing of genotypic variance heterogeneity to detect
Description: Presence of interaction between a SNP and another SNP (or another factor) can result
in heterogeneity of variance between the genotypes of an interacting SNP.
Detecting such heterogeneity gives prior knowledge for constructing a genetic model

Title: Testing of Genotypic Variance Heterogeneity to Detect

Title: High-Dimensional Screening for Semiparametric Longitudinal
person("Runze","Li", email = "runzelipsu@gmail.com", role = "aut"),
person("Wanghuan","Chu", email = "dqchuwh@gmail.com", role = "aut"),
person("Liying","Huang", email = "lxh37@PSU.EDU", role = c("aut","cre")),
Description: Implements a screening procedure proposed by Chu, Li, and
Reimherr (2016) for varying coefficient longitudinal models with ultra-
The effect of each predictor is allowed to vary over time, approximated by a
low-dimensional B-spline. Within-subject correlation is handled using a
generalized estimation equation approach with structure specified by the user.
Variance is allowed to change over time, also approximated by a B-spline.

Description: Implements a screening procedure proposed by Wanghuan Chu, Runze Li
and Matthew Reimherr (2016) <DOI:10.1214/16-AOAS912> for varying coefficient
longitudinal models with ultra-high dimensional predictors . The effect of each
predictor is allowed to vary over time, approximated by a low-dimensional B-spline.
Within-subject correlation is handled using a generalized estimation equation
approach with structure specified by the user. Variance is allowed to change
over time, also approximated by a B-spline.

Description: Implements variable screening techniques for ultra-high
dimensional regression settings.  Techniques for independent (iid) data,
varying-coefficient models, and longitudinal data are implemented. The package
currently contains three screen functions: screenIID(), screenLD() and screenVCM(),
and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(),
simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of
Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) <DOI:10.1198/jasa.2011.tm10563>,
Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) <DOI:10.1214/16-AOAS912>.
Special thanks are due to Ling Zhang for providing detailed testing and proposing
a method for speed improvement on the simulation of data with AR-1 structure.
Imports: gee, expm, splines, MASS, energy