Data sets and scripts for Modeling Psychophysical Data in R
Data sets and scripts for Modeling Psychophysical Data in
R

Data sets and scripts for Modeling Psychophysical Data in R.
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/

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

Data Sets and Scripts for Modeling Psychophysical Data in R

A Two-Step Approach to Testing Overall Effect of
Gene-Environment Interaction for Multiple Phenotypes
c(person("Arunabha", "Majumdar", email = "statgen.arunabha@gmail.com", role = c("aut", "cre")), person("Tanushree", "Haldar", email = "tanushree.haldar@gmail.com", role = "aut"))
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>.
Arunabha Majumdar [aut, cre],
Tanushree Haldar [aut]

Computation of Multidimensional Poverty Index (MPI)
c(
person(given = "Kittiya",
family = "Kukiattikun",
role = c("aut", "cre"),
email = "kittiya.contact@gmail.com"),
person(given = "Chainarong",
family = "Chainarong",
role = "aut",
email = "grandca@gmail.com"
))
Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold.  The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator.  Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) <doi:10.35648/20.500.12413/11781/ii039> for The Alkire-Foster methodology.
dplyr, tidyr, utils, doParallel, foreach, purrr
Kittiya Kukiattikun [aut, cre],
Chainarong Chainarong [aut]