Depends: R (>= 3.1.0), graphics, stats, utils
Description: It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.

Package: MPCI
Type: Package
Title: Multivariate Process Capability Indices (MPCI)
Version: 1.0
Date: 2011-08-20
Author: Edgar Santos Fernandez, Michele Scagliarini.
Maintainer: Edgar Santos Fernandez <edgar.santos@etecsa.cu>
Description: MPCI package performs the followings Multivariate Process
Capability Indices: Shahriari et al. (1995) Multivariate
Capability Vector, Taam et al. (1993) Multivariate Capability
Index (MCpm) and the followings based on Principal Componenet
Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis
(2002) and Wang (2005). Moreover, include two datasets.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2011-09-12 15:22:11 UTC; edgars
Repository: CRAN
Date/Publication: 2011-09-15 20:18:54

Title: Data sets and scripts for Modeling Psychophysical Data in R
Description: Data sets and scripts for Modeling Psychophysical Data in

Title: Data sets and scripts for Modeling Psychophysical Data in R.
Description: Data sets and scripts for Modeling Psychophysical Data in R (Springer).  The lme4.0 package, used only in conjunction with the polmer function, is available from R-Forge at http://https://r-forge.r-project.org/projects/lme4/

Description: Data sets and scripts for Modeling Psychophysical Data in R (Springer).

Title: Data Sets and Scripts for Modeling Psychophysical Data in R

Title: A Two-Step Approach to Testing Overall Effect of
Gene-Environment Interaction for Multiple Phenotypes
Authors@R: c(person("Arunabha", "Majumdar", email = "statgen.arunabha@gmail.com", role = c("aut", "cre")), person("Tanushree", "Haldar", email = "tanushree.haldar@gmail.com", role = "aut"))
Description: Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1101/2020.07.06.190256>.