Description: Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations?  This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.

Title: Calculate the Probability of Correct Selection (PCS)

Title: Parallel Constraint Satisfaction Networks in R
Authors@R: person("Felix", "Henninger", email = "mailbox@felixhenninger.com", role = c("aut", "cre"))
Description: Parallel Constraint Satisfaction (PCS) models are an increasingly
common class of models in Psychology, with applications to reading and word
recognition (McClelland & Rumelhart, 1981), judgment and decision making
(Glöckner & Betsch, 2008; Glöckner, Hilbig, & Jekel, 2014), and several
other fields (e.g. Read, Vanman, & Miller, 1997). In each of these fields,
they provide a quantitative model of psychological phenomena, with precise
predictions regarding choice probabilities, decision times, and often the degree
of confidence. This package provides the necessary functions to create and
simulate basic Parallel Constraint Satisfaction networks within R.
Packaged: 2016-10-19 10:57:29 UTC; felixhenninger

Depends: gtools, psych, GPArotation, ThreeWay, MASS
Description: Analyzing regression data with many and/or highly
collinear predictor variables, by simultaneously reducing the
predictor variables to a limited number of components and
regressing the criterion variables on these components. Several
rotation options are provided in this package, as well as model
Author: Marlies Vervloet [aut, cre], Henk Kiers [aut], Eva Ceulemans

Depends: GPArotation, ThreeWay, MASS, Matrix
Description: Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components. Several rotation options are provided in this package, as well as model selection options.

Depends: GPArotation, ThreeWay, MASS, stats, graphics, Matrix
Description: Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components (de Jong S. & Kiers H. A. L. (1992) <doi:10.1016/0169-7439(92)80100-I>). Several rotation and model selection options are provided.