A Button-Based GUI for Financial and Economic Data Analysis
A GUI designed to support the analysis of financial-economic time series data.

A GUI designed to support the analysis of financial-economic time
series data.

fBasics, forecast, grid, lubridate, openair, timeDate,
timeSeries

boot, car, coefplot, fBasics, forecast, grid, lmtest,
lubridate, openair, papeR, timeDate, timeSeries

boot, car, coefplot, fBasics, forecast, grid, lmtest,
lubridate, openair, papeR, timeDate, timeSeries, zoo

Integrative clustering of multiple genomic data types
Integrative clustering of multiple genomic data types
using a joint latent variable model with application to breast
and lung cancer subtype analysis.

R (>= 2.15.0), lattice, caTools, gdata, gtools, gplots,
parallel
Integrative clustering of multiple genomic data types
using a joint latent variable model.

c(person("Ya-Shan", "Cheng", role = c("aut", "cre"),email =
"acidnoodle@hotmail.com"), person("Chien-Yu", "Peng", role =
c("aut", "cre"),email = "chienyu@stat.sinica.edu.tw"))
The package iDEMO written in R and R GUI is a tool to
build a linear degradation model which can simultaneously
consider the unit-to-unit variation, time-dependent structure
and measurement error in the degradation paths. Moreover, we
provide the maximum likelihood estimates (MLEs) of the unknown
parameters, mean-time-to-failure (MTTF) and qth quantile, and
their corresponding confidence intervals.

Integrative Differential Network Analysis in Genomics
mvtnorm, glasso, parallel, GGMridge, visNetwork, scales
Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups.

Min Jin Ha <mjha@mdanderson.org>, Caleb A. Class <caclass@mdanderson.org>
Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The method is described in Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.

Caleb A. Class <caclass@mdanderson.org>, Min Jin Ha <mjha@mdanderson.org>
Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) <doi:10.1093/bioinformatics/btx750> and Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.