Fit mixed-effects location scale models with spike-and-slab priors on the location random effects to identify units with unusual residual variances. The method is described in detail in Carmo, Williams and Rast (2025) <https://osf.io/sh6ne>.

Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2020) <doi:10.1017/pan.2019.48> and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) <doi:10.48550/arXiv.2103.06328>.

Estimation and diagnostic tools for instrumental variables designs, which implements the guidelines proposed in Lal et al. (2023) <arXiv:2303.11399>, including bootstrapped confidence intervals, effective F-statistic, Anderson-Rubin test, valid-t ratio test, and local-to-zero tests.

Instrumental variable (IV) estimators for homogeneous and
    heterogeneous treatment effects with efficient machine learning instruments.
    The estimators are based on double/debiased machine learning allowing for
    nonlinear and potentially high-dimensional control variables. Details can 
    be found in Scheidegger, Guo and Bühlmann (2025) "Inference for 
    heterogeneous treatment effects with efficient instruments and machine 
    learning" <doi:10.48550/arXiv.2503.03530>.

Uses data and researcher's beliefs on measurement error and
    instrumental variable (IV) endogeneity to generate the space of consistent 
    beliefs across measurement error, instrument endogeneity, and instrumental 
    relevance for IV regressions. 
    Package based on DiTraglia and Garcia-Jimeno (2020) <doi:10.1080/07350015.2020.1753528>.

Provides facilities of general to specific model selection for
    exogenous regressors in 2SLS models. Furthermore, indicator saturation
    methods can be used to detect outliers and structural breaks in the sample.

Implements network-aware instrumental variable regression
    for causal node discovery in high-dimensional settings with
    graph-structured exposures. Provides IVGL and IVGL-S estimators
    combining graph-Laplacian penalization with IV-based identification,
    including correction for invalid instruments via a sisVIVE-style
    update. Methods are described in Pal and Ghosh (2026)
    <doi:10.48550/arXiv.2604.24969>. The 'glmgraph' package, required
    for the main estimators, is available at the additional repository
    <https://djghosh1123.r-universe.dev>.