Description: Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) <doi:10.18637/jss.v076.c01>).

Author: David Magis (Belgium), Gilles Raiche (UQAM, Canada), Juan Ramon Barrada (U Zaragoza, Spain)

Description: Generation of response patterns under computerized adaptive testing (CAT) framework, with the choice of several item response theory (IRT) models, starting rules, next item selection routines, stopping rules and ability estimators.

Title: Encode Categorical Variables with Split Information from CART
Description: Use primary and surrogate split information from CART (Classification and Regression Trees - Breiman L (1984)) as the
vector representation for a categorical variable. Outputs binary
columns for each categorical variable making use of target information.
Imports: rpart, caret, dplyr, stats, utils, data.table, stringr,

Title: Computerized Adaptive Testing for Survey Research
Authors@R: c(person("Jacob", "Montgomery", email =  "jacob.montgomery@wustl.edu", role = "aut"), person("Erin", "Rossiter", email = "erinrossiter@wustl.edu", role = c("aut", "cre")))
Description: Provides methods of computerized adaptive testing for survey researchers. Includes functionality for data fit with the classic item response methods including the latent trait model, Birnbaum`s three parameter model, the graded response, and the generalized partial credit model.  Additionally, includes several ability parameter estimation and item selection routines.  During item selection, all calculations are done in compiled C++ code.
LinkingTo: BH(>= 1.62.0.1), Rcpp(>= 0.12.11), RcppArmadillo, RcppGSL(>=
Packaged: 2017-06-15 16:40:05 UTC; erinrossiter