Packaged: 2018-06-05 19:20:16 UTC; jonathanlisic

Packaged: 2020-06-26 21:55:59 UTC; jonathanlisic

Title: Cause-Deleted Life Expectancy Improvement Procedure
Author: Peter Adamic, Alicja Wolny-Dominiak
Description: The concept of cause-deleted life expectancy improvement is statistic designed to quantify the increase in life expectancy if a certain cause of death is removed. See Adamic, P. (2015) (<https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2689352>).

Title: Useful Tools for Cognitive Diagnosis Modeling
Authors@R: c(person(given = "Pablo", family = "Nájera", role = c("aut", "cre", "cph"), email = "pablo.najera@uam.es"), person(given = "Miguel A.", family = "Sorrel", role = c("aut", "cph"), email = "miguel.sorrel@uam.es"), person(given = "Francisco J.", family = "Abad", role = c("aut", "cph")))
Description: Provides useful tools for cognitive diagnosis modeling (CDM). The packages includes the discrete factor loading method for Q-matrix estimation (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>) and the Hull method for Q-matrix validation (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>). It also provides dimensionality assessment procedures for determining the number of attributes underlying CDM data, including parallel analysis and automated CDM fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.

Authors@R: c(person(given = "Pablo", family = "Nájera", role = c("aut", "cre", "cph"), email = "pablo.najera@uam.es"), person(given = "Miguel A.", family = "Sorrel", role = c("aut", "cph"), email = "miguel.sorrel@uam.es"), person(given = "Francisco J.", family = "Abad", role = c("aut", "cph")), person(given = "Rodrigo S.", family = "Kreitchmann", role = c("ctb")))
Description: Provides useful tools for cognitive diagnosis modeling (CDM). The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>) and the discrete factor loading method (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Other relevant methods and features for CDM applications, such as the general nonparametric classification method (Chiu et al., 2018; <doi:10.1007/s11336-017-9595-4>) and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.