Provides a comprehensive set of datasets and tools for 'causal inference' research. 
    The package includes data from clinical trials, cancer studies, epidemiological surveys, environmental exposures, and health-related observational studies.
    Designed to facilitate causal analysis, risk assessment, and advanced statistical modeling, 
    it leverages datasets from packages such as 'causalOT', 'survival', 'causalPAF', 'evident', 'melt', and 'sanon'. 
    The package is inspired by the foundational work of Pearl (2009) <doi:10.1017/CBO9780511803161> on causal inference frameworks.

Fits, simulates, and evaluates forced-choice and traditional
    item response theory (IRT) models for noncognitive assessment. Eight model
    families are supported, spanning dominance (multidimensional IRT (MIRT)
    1PL--4PL; multidimensional generalized partial credit model (MGPCM)),
    ideal-point unfolding (multidimensional generalized graded unfolding
    model (MGGUM)), and forced-choice designs (forced-choice multidimensional
    IRT (FCMIRT), forced-choice generalized graded unfolding model (FCGGUM),
    Thurstonian IRT (TIRT), forced-choice diagnostic classification model
    (FCDCM), forced-choice generalized deterministic inputs, noisy "and" gate
    model (FCGDINA)) that mitigate response biases such as acquiescence and
    social desirability. Core estimation backends include full Bayesian
    inference via Hamiltonian Monte Carlo (Stan) and a fast improved
    stochastic expectation-maximization (iStEM) algorithm suitable for
    large-scale data; FCGDINA also provides a deterministic
    expectation-maximization (EM) estimator. Comprehensive model evaluation
    uses the limited-information M2 family of goodness-of-fit statistics
    (Maydeu-Olivares and Joe, 2005 <doi:10.1198/016214504000002069>; 2006
    <doi:10.1007/s11336-005-1295-9>) together with root mean square error of
    approximation (RMSEA), comparative fit index (CFI), Tucker-Lewis index
    (TLI), and standardized root mean square residual (SRMSR).