Estimates the local average treatment effect (LATE) and the
    local average treatment effect on the treated (LATT) using observational
    data with a binary instrument, implementing the complete estimator suite
    of Sloczynski, Uysal, and Wooldridge: the doubly robust estimators of
    Sloczynski, Uysal, and Wooldridge (2022) <doi:10.48550/arXiv.2208.01300>
    -- inverse probability weighted regression adjustment (IPWRA), inverse
    probability weighting (IPW), augmented inverse probability weighting
    (AIPW), and regression adjustment (RA) -- and the Abadie-kappa weighting
    estimators of Sloczynski, Uysal, and Wooldridge (2025)
    <doi:10.1080/07350015.2024.2332763>. Supports linear, logistic, probit,
    Poisson, and fractional (fractional-logit and fractional-probit) outcome
    and treatment models, and instrument propensity scores
    estimated by maximum likelihood, covariate balancing (CBPS), or inverse
    probability tilting (IPT). Standard errors are computed jointly for all
    estimation stages by stacking the moment conditions of every model into
    a single M-estimation system; weak-instrument-robust Fieller confidence
    sets, cluster-aware bootstrap inference, design diagnostics, and a
    doubly robust Hausman-type test of unconfoundedness are included.
    Estimates and standard errors are validated against the authors' Stata
    commands 'drlate' (Statistical Software Components S459708) and
    'kappalate' (S459257).

Implements Doubly Robust Matching-Adjusted Indirect Comparison 
    (DR-MAIC) for population-adjusted indirect treatment comparisons in health 
    technology appraisal (HTA). The package provides: (1) standard MAIC via 
    entropy balancing / exponential tilting; (2) augmented/doubly robust MAIC 
    combining inverse probability weighting with outcome regression; (3) 
    comprehensive covariate balance diagnostics including standardised mean 
    differences, Love plots, and effective sample size; (4) sensitivity analyses 
    including E-values, weight trimming, and variable exclusion analyses; (5) 
    bootstrap confidence intervals; and (6) submission-ready outputs aligned with 
    NICE Decision Support Unit Technical Support Document 18, Cochrane Handbook 
    guidance on indirect comparisons, and ISPOR best practice guidelines.
    Supports binary (risk difference, risk ratio, odds ratio) and time-to-event 
    (hazard ratio) outcomes.