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>).

Downloads USDA National Agricultural Statistics Service (NASS) 
    cropscape data for a specified state. Utilities for fips, abbreviation, 
    and name conversion are also provided. Full functionality requires an 
    internet connection, but data sets can be cached for later off-line use.

Functions for cognitive diagnosis modeling and multidimensional item response modeling 
    for dichotomous and polytomous item responses. This package enables the estimation of 
    the DINA and DINO model (Junker & Sijtsma, 2001, <doi:10.1177/01466210122032064>),
    the multiple group (polytomous) GDINA model (de la Torre, 2011, 
    <doi:10.1007/s11336-011-9207-7>), the multiple choice DINA model (de la Torre, 2009, 
    <doi:10.1177/0146621608320523>), the general diagnostic model (GDM; von Davier, 2008, 
    <doi:10.1348/000711007X193957>), the structured latent class model (SLCA; Formann, 1992, 
    <doi:10.1080/01621459.1992.10475229>) and regularized latent class analysis 
    (Chen, Li, Liu, & Ying, 2017, <doi:10.1007/s11336-016-9545-6>). 
    See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> 
    or Robitzsch and George (2019, <doi:10.1007/978-3-030-05584-4_26>)     
    for further details on estimation and the package structure.
    For tutorials on how to use the CDM package see 
    George and Robitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as
    Ravand and Robitzsch (2015).

Provides tools for working with observational health data in the 
  Observational Medical Outcomes Partnership (OMOP) Common Data Model format with a pipe friendly syntax.
  Common data model database table references are stored in a single compound object along with metadata.

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 restricted DINA model (Nájera et al., 2023; <doi:10.3102/10769986231158829>), 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; <doi:10.3758/s13428-022-01967-5>) 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.