Title: Bias-Corrected Estimates for Generalized Linear Models for
Author: Nicola Lunardon <nicola.lunardon@unimib.it>, Daniel Scharfstein <dscharf@jhu.edu>
Description: Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.

Title: A Dirichlet Process Mixture Model for Clustering Longitudinal
Author: Jiehuan Sun [aut, cre], Jose D. Herazo-Maya[aut], Naftali Kaminski[aut], Hongyu Zhao [aut], and Joshua L. Warren [aut],
Description: Many clustering methods have been proposed, but
most of them cannot work for longitudinal gene expression data.
'BClustLonG' is a package that allows us to perform clustering analysis for
longitudinal gene expression data. It adopts a linear-mixed effects framework
to model the trajectory of genes over time, while clustering is jointly
conducted based on the regression coefficients obtained from all genes.
To account for the correlations among genes and alleviate the
high dimensionality challenges, factor analysis models are adopted
for the regression coefficients. The Dirichlet process prior distribution
is utilized for the means of the regression coefficients to induce clustering.
This package allows users to specify which variables to use for clustering
(intercepts or slopes or both) and whether a factor analysis model is desired.
More details about this method can be found in Jiehuan Sun, et al. (2017) <doi:10.1002/sim.7374>.
Packaged: 2017-12-14 19:23:01 UTC; JiehuanSun

Packaged: 2017-12-15 13:21:23 UTC; JiehuanSun

Packaged: 2020-05-07 03:18:24 UTC; JiehuanSun

Title: Efficient Determination of Sample Size in Balanced Design of
Maintainer: Jong Hee Chung <jochung947@gmail.com>
Description: Provides the sample size in balanced design of experiments and three graphs ; detectable standardized effect size vs power, sample size vs detectable standardized effect size, and sample size vs power.
Sample size is computed in order to detect a certain standardized effect size with power at the significance level.
Three graphs show the mutual relationship between the sample size, power and the detectable standardized effect size.
By investigating those graphs, it can be checked that which effects are sensitive to the efficient sample size determination.
Montgomery, Douglas C. (2013, ISBN: 0849323312).
Imports: fpow, shiny, shinyalert, ggplot2