Provides robustness checks to align estimands with the identification 
    that they require. Given a 'dagitty' object and a model specification, 
    'DAGassist' classifies variables by causal roles, flags problematic controls, 
    and generates a report comparing the original model with minimal and canonical 
    adjustment sets. Exports publication-grade reports in 'LaTeX', 'Word', 'Excel',
    'dotwhisker', or plain text/'markdown'. 'DAGassist' is built on 'dagitty', an 
    'R' package that uses the 'DAGitty' web tool (<https://dagitty.net/>) for 
    creating and analyzing DAGs. Methods draw on Pearl (2009) <doi:10.1017/CBO9780511803161>
    and Textor et al. (2016) <doi:10.1093/ije/dyw341>.

Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification. HMMs are the Legos of computational sequence analysis. In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph. Tree represents the nodes connected by edges. It is a non-linear data structure. A poly-tree is simply a directed acyclic graph whose underlying undirected graph is a tree. The model proposed in this package is the same as an HMM but where the states are linked via a polytree structure rather than a simple path.

Compressed spatial vector data originally from <https://dawadocs.dataforsyningen.dk/> saved as Simple 
    Features, SF, objects with data on population, age and gender from Statistics Denmark <https://www.dst.dk/da/>.

A port of the web-based software 'DAGitty', available at 
    <https://dagitty.net>, for analyzing structural causal models 
    (also known as directed acyclic graphs or DAGs).
    This package computes covariate adjustment sets for estimating causal
    effects, enumerates instrumental variables, derives testable
    implications (d-separation and vanishing tetrads), generates equivalent
    models, and includes a simple facility for data simulation.