Title: Data Sets for Psychometric Modeling
Description: Collection of data sets from various assessments that can be used to
evaluate psychometric models. These data sets have been analyzed in the
following papers that introduced new methodology as part of the application section:

Title: Empirical Differentially Methylated Regions Calculation
Description: Optimized DMR analysis based on bimodal normal distribution
model and cost function for regional methylation analysis optimization.
It captures the regional methylation modification by taking the spatial
distribution of CpGs into account for the enrichment DNA methylation sequencing
data so as to optimize the definition of the empirical regions. Combined with
the dependent adjustment for regional p-value combination and DMR annotation.
Authors@R: c(person(given="Sheng", family="Li", role=c("aut","cre", "cph"), email = "shelly1436@gmail.com"),
Imports: GenomicRanges, IRanges, data.table, mixtools, S4Vectors
Collate: 'ACF.R' 'edmr.R' 'eDMR.sub.R' 'get.dist.cutoff.R' 'getDMR.R'

Title: Empirical differentially methylated regions calculation
Imports: GenomicRanges, IRanges, data.table, mixtools

Description: R client for Empirical Data Platform. More information is at <https://empirical.com>. For support, contact support@empirical.com.
Imports: httr, ini, jsonlite, plyr, stats
Packaged: 2017-05-27 20:54:38 UTC; madeleine

Packaged: 2017-05-30 22:29:34 UTC; madeleine

Packaged: 2017-08-24 23:02:20 UTC; madeleine

Imports: httr, ini, jsonlite, methods, plyr, stats
Packaged: 2018-03-05 16:56:19 UTC; madeleine

Title: Provide tools for dimension reduction methods
Author: Benoit Liquet and Jerome Saracco
Description: This package comes to illustrate the article "A graphical
tool for selecting the number of slices and the dimension of

Title: Provides tools for dimension reduction methods
Author: Raphaël Coudret, Benoît Liquet and Jérôme Saracco
Description: This package comes to illustrate the articles "A graphical
the model in SIR and SAVE approaches" and "Comparison of sliced
inverse regression approaches for underdetermined cases"