Provides tools to conduct interpretable sensitivity analyses for weighted estimators,
    introduced in Huang (2024) <doi:10.1093/jrsssa/qnae012> and 
    Hartman and Huang (2024) <doi:10.1017/pan.2023.12>. 
    The package allows researchers to generate the set of recommended sensitivity summaries
    to evaluate the sensitivity in their underlying weighting estimators to omitted moderators or confounders. 
    The tools can be flexibly applied in causal inference settings (i.e., in external and internal validity contexts)
    or survey contexts.

Sensitivity analysis for trials with irregular and informative 
    assessment times, based on a new influence function-based, augmented 
    inverse intensity-weighted estimator.

Provides an interface to the 'Sensibo Sky' API which allows to remotely control non-smart air conditioning units.
  See <https://sensibo.com> for more informations.

An implementation of sensitivity analysis for phylogenetic comparative
 methods. The package is an umbrella of statistical and graphical methods that 
 estimate and report different types of uncertainty in PCM:
 (i) Species Sampling uncertainty (sample size; influential species and clades).
 (ii) Phylogenetic uncertainty (different topologies and/or branch lengths).
 (iii) Data uncertainty (intraspecific variation and measurement error).

A collection of functions for sensitivity analysis of model outputs (factor screening, global sensitivity analysis and robustness analysis), for variable importance measures of data, as well as for interpretability of machine learning models. Most of the functions have to be applied on scalar output, but several functions support multi-dimensional outputs.

Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.

Sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition.  The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468).  The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al.  The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al.  The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test.  The other functions in the package are internal functions.