Provides doubly robust one-step and targeted maximum likelihood
    (TMLE) estimators for average causal effects in acyclic directed mixed
    graphs (ADMGs) with unmeasured variables. Automatically determines whether
    the treatment effect is identified via backdoor adjustment or the extended
    front-door functional, and dispatches to the appropriate estimator.
    Supports incorporation of machine learning algorithms via 'SuperLearner'
    and cross-fitting for nuisance estimation. Methods are described in Guo and Nabi (2024) <doi:10.48550/arXiv.2409.03962>.

The main function kcca implements a general framework for
  k-centroids cluster analysis supporting arbitrary distance measures
  and centroid computation. Further cluster methods include hard
  competitive learning, neural gas, and QT clustering. There are
  numerous visualization methods for cluster results (neighborhood
  graphs, convex cluster hulls, barcharts of centroids, ...), and
  bootstrap methods for the analysis of cluster stability.

An implementation of multiple regression models for 
    count data. These include various forms of the negative binomial (NB-1, 
    NB-2, NB-P, generalized negative binomial, etc.), Poisson-Lognormal, other
    compound Poisson distributions, the Generalized Waring model, etc.
    Information on the different forms of the negative binomial are
    described by Greene (2008) <doi:10.1016/j.econlet.2007.10.015>. For
    treatises on count models, see Cameron and Trivedi (2013) 
    <doi:10.1017/CBO9781139013567> and Hilbe (2012)
    <doi:10.1017/CBO9780511973420>. For the implementation of
    under-reporting in count models, see Wood et al. (2016)
    <doi:10.1016/j.aap.2016.06.013>. For prediction methods in random
    parameter models, see Wood and Gayah (2025)
    <doi:10.1016/j.aap.2025.108147>. For estimating random parameters using
    maximum simulated likelihood, see Greene and Hill (2010)
    <doi:10.1108/S0731-9053(2010)26>; Gourieroux and Monfort (1996)
    <doi:10.1093/0198774753.001.0001>; or Hensher et al. (2015)
    <doi:10.1017/CBO9781316136232>.