The framework of causal decomposition of group disparities developed by
    Yu and Elwert (2023)
    <doi:10.48550/arXiv.2306.16591>.
    This package implements the decomposition estimators
    that are based on efficient influence functions. For the
    nuisance functions of the estimators, both parametric and
    nonparametric options are provided, as well as manual options in case
    the default models are not satisfying.

Estimates hidden Markov models from the family of Cholesky-decomposed Gaussian hidden Markov models (CDGHMM) under various missingness schemes. This family improves upon estimation of traditional Gaussian HMMs by directly modelling the distinct correlation structures that arise from longitudinal data, as well as, controlling for dropped out observations and non-random missingness. See Neal, Sochaniwsky and McNicholas (2024) <DOI:10.48550/arXiv.2404.04122>.

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).