For a manual on how to use 'cAIC4', see Saefken et al. (2021) <doi:10.18637/jss.v099.i08>.

Authors@R: c(person("Steve", "Culpepper", role = c("aut", "cph"), email =
"sculpepp@illinois.edu"), person("James", "Balamuta", role = c("aut",
"cre", "ctb", "com","cph"),email = "balamut2@illinois.edu"))
Description: Jointly model the accuracy of cognitive responses and item choices within a bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.
James Balamuta [aut, cre, ctb, com, cph]

Description: Jointly model the accuracy of cognitive responses and item choices
within a bayesian hierarchical framework as described by Culpepper and
contains the datasets used within the analysis of the paper.

LinkingTo: Rcpp, RcppArmadillo (>= 0.9.200)
Author: Steven Andrew Culpepper [aut, cph]

within a Bayesian hierarchical framework as described by Culpepper and

LinkingTo: Rcpp, RcppArmadillo (>= 0.10.8.1.0)

Title: cMonkey intgrated biclustering algorithm
Author: David J Reiss, Institute for Systems Biology
Description: cMonkey integrated biclustering algorithm for learning
Suggests: RCurl, foreach, doMC, multicore, ff, igraph, RSVGTipsDevice,

Suggests: RCurl, doMC, foreach, multicore, igraph, RSVGTipsDevice,

Title: cMonkey integrated biclustering algorithm
Suggests: RCurl, doMC, foreach, multicore, igraph0, RSVGTipsDevice,

Author: Wolfgang Lenhard [cre, aut] (<https://orcid.org/0000-0002-8184-6889>),
Authors@R: c(person("Wolfgang", "Lenhard", role = c("cre","aut"), email="wolfgang.lenhard@uni-wuerzburg.de", comment=c(ORCID  = "0000-0002-8184-6889")), person("Alexandra", "Lenhard", role=c("aut")), person("Sebastian", "Gary", role=c("ctb")))
Description: Conventional methods for producing standard scores in psychometrics or biometrics are often plagued
with "jumps" or "gaps" (i.e., discontinuities) in norm tables and low confidence for assessing extreme scores.
The continuous norming method introduced by A. Lenhard et al. (2016), <doi:10.1177/1073191116656437>,  generates
continuous test norm scores on the basis of the raw data from standardization samples,
without requiring assumptions about the distribution of the raw data: Norm scores are directly established
from raw data by modeling the latter ones as a function of both percentile scores and an explanatory
variable (e.g., age). The method minimizes bias arising from sampling and measurement error, while
handling marked deviations from normality, addressing bottom or ceiling effects and capturing almost
all of the variance in the original norm data sample.
Imports: lattice (>= 0.20), leaps (>= 3.0.0), latticeExtra (>= 0.6)
Suggests: knitr, rmarkdown, shiny, shinycssloaders, foreign, readxl