Title: Estimate the Coefficients for Non-Convex Penalized Quantile
Regression Model by using QICD Algorithm
Authors@R: c(person("Bo", "Peng", role = c("aut", "cre"),
person("Rondall", "E. Jones", role = "ctb"),
person("John","A. Wisniewski", role = "ctb"))
Description: Extremely fast algorithm "QICD", Iterative Coordinate
Descent Algorithm for High-dimensional Nonconvex Penalized Quantile
Regression. This algorithm combines the coordinate descent algorithm
in the inner iteration with the majorization minimization step
in the outside step. For each inner univariate minimization problem,
we only need to compute a one-dimensional weighted median,
which ensures fast computation. Tuning parameter selection is based
on two different method: the cross validation and BIC for
quantile regression model. Details are described in the Peng,B and Wang,L. (2015)

quantile regression model. Details are described in Peng,B and Wang,L. (2015)

Title: Quantitative Key Performance Indicators
Authors@R: c(person("Abubakar", "Akanbi", role = c("aut", "cre"),
Description: It calculates key performance indicators as quantitative metrics for sales and
overall labour effectiveness. Wikipedia (2021)

Description: This package is used to apply the QL, QLShrink, and
QLSpline methods (Lund and Nettleton, 2011) for identifying
differentially expressed genes in RNA-seq data.

Title: Reinforcement Learning using the Q Learning Algorithm
Description: Implements Q-Learning, a model-free form of reinforcement
learning, described in work by Strehl, Li, Wiewiora, Langford &
Packaged: 2017-09-20 21:39:02 UTC; liambressler

Title: Differential Methylation Analysis with
Description: Differential methylation analysis. This package is developed for estimating
variance and mean dependence in count data from MeRIP-seq and test for
differential methylation based on a model using

Title: Differential RNA Methylation Analysis with
Description: We present QNB, a statistical approach for differential RNA
methylation analysis with count-based small-sample sequencing
data. The method is based on 4 independent negative binomial
distributions, and with their variances and means linked by
local regressions, respectively. Please don't hesitate to
contact <liulian19860905@163.com> if you have any questions.