Title: High-Dimensional Regression and CAR Score Variable Selection
described in Zuber and Strimmer (2011).  CAR scores measure the
canonical ordering of variables - see Zuber and Strimmer (2011)
for details. This package provides functions for estimating CAR
scores, for variable selection using CAR scores, and for
estimating corresponding regression coefficients. Both
shrinkage as well as empirical estimators are available.

Description: The "care" package implements the regression approach of
Zuber and Strimmer (2011) "High-dimensional regression and
variable selection using CAR scores" SAGMB 10: 34. CAR scores
measure the correlation between the response and the
canonical ordering of variables. This package provides
functions for estimating CAR scores, for variable selection
using CAR scores, and for estimating corresponding regression
coefficients. Both shrinkage as well as empirical estimators

Description: Implements the regression approach
of Zuber and Strimmer (2011) "High-dimensional regression and variable
CAR scores measure the correlation between the response and the
Mahalanobis-decorrelated  predictors.  The squared CAR score is a
natural measure of variable importance and provides a canonical
ordering of variables. This package provides functions for estimating
CAR scores, for variable selection using CAR scores, and for estimating
corresponding regression coefficients. Both shrinkage as well as

variable selection using CAR scores" SAGMB 10: 34.  CAR scores

Description: The "care" package implements the regression approach
selection using CAR scores" SAGMB 10: 34.
natural measure of variable  importance and provides a canonical

Title: Procedures for Computing Indices of Careless Responding
person("Richard", "Yentes" , email = "rdyentes@ncsu.edu", role = c("cre", "aut")),
person("Francisco", "Wilhelm", email = "franciscowilhelm@gmail.com", role = c("aut")))
Description: When taking online surveys, participants sometimes respond to items without regard to their content.
These types of responses, referred to as careless or insufficient effort responding, constitute significant problems for data quality, leading to distortions in data analysis and hypothesis testing, such as spurious correlations. The 'R' package 'careless' provides solutions designed to detect such careless / insufficient effort responses by allowing easy calculation of indices proposed in the literature. It currently supports the calculation of longstring, even-odd consistency, psychometric synonyms/antonyms, Mahalanobis distance, and intra-individual response variability (also termed inter-item standard deviation). For a review of these methods, see Curran (2016) <doi:10.1016/j.jesp.2015.07.006>.