rquery (>= 1.4.5), rqdatatable (>= 1.2.8), methods, stats
DBI, RSQLite, knitr, rmarkdown, yaml, tinytest

c(person("Emilio", "Torres Manzanera", role = c("aut",
"cre"), email = "torres@uniovi.es"))
A constant database is a data structure created by Daniel
J. Bernstein in his cdb package. Its format consists on a
sequence of (key,value)-pairs. This R package replicates the
basic utilities for reading (cdbget) and writing (cdbdump)
constant databases.

Cognitive Diagnostic Computerized Adaptive Testing
c(person(given = "Miguel A.",family = "Sorrel", role = c("aut", "cre", "cph"),email = "miguel.sorrel@uam.es"),person(given = "Pablo", family = "Nájera", role = c("aut","cph")), person(given="Francisco J.",family = "Abad",role = c("aut","cph")))
Provides a set of functions for conducting cognitive diagnostic computerized adaptive testing applications (Chen (2009) <doi:10.1007/s11336-009-9123-2>). It includes different item selection rules such us the global discrimination index (Kaplan, de la Torre, and Barrada (2015) <doi:10.1177/0146621614554650>) and the nonparametric selection method (Chang, Chiu, and Tsai (2019) <doi:10.1177/0146621618813113>), as well as several stopping rules. Functions for generating item banks and responses are also provided. To guide item bank calibration, model comparison at the item level can be conducted using the two-step likelihood ratio test statistic by Sorrel, de la Torre, Abad and Olea (2017) <doi:10.1027/1614-2241/a000131>.
GDINA (>= 2.2.0), ggplot2 (>= 3.3.0), cowplot, foreach,
doSNOW, NPCD, stats
Miguel A. Sorrel [aut, cre, cph],
Pablo Nájera [aut, cph],
Francisco J. Abad [aut, cph]

Provides a set of functions for conducting cognitive diagnostic computerized adaptive testing applications (Chen, 2009) <DOI:10.1007/s11336-009-9123-2>). It includes different item selection rules such us the global discrimination index (Kaplan, de la Torre, and Barrada (2015) <DOI:10.1177/0146621614554650>) and the nonparametric selection method (Chang, Chiu, and Tsai (2019) <DOI:10.1177/0146621618813113>), as well as several stopping rules. Functions for generating item banks and responses are also provided. To guide item bank calibration, model comparison at the item level can be conducted using the two-step likelihood ratio test statistic by Sorrel, de la Torre, Abad and Olea (2017) <DOI:10.1027/1614-2241/a000131>.