Implements the procedure from G. J. Ross (2021) - "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" <arxiv:2107.01742>. This uses a version of Wild Binary Segmentation to detect multiple location-scale (i.e. mean and/or variance) change points in a sequence of univariate observations, with a strict control on the probability of incorrectly detecting a change point in a sequence which does not contain any.

Facilitates nonresponse bias analysis (NRBA) 
    for survey data.  Such data may arise from a complex
    sampling design with features such as stratification, clustering, or
    unequal probabilities of selection. Multiple types of analyses may be
    conducted: comparisons of response rates across subgroups; comparisons
    of estimates before and after weighting adjustments; comparisons of
    sample-based estimates to external population totals; tests of
    systematic differences in covariate means between respondents
    and full samples; tests of independence between response status
    and covariates; and modeling of outcomes and response status as a 
    function of covariates. Extensive documentation and references are
    provided for each type of analysis. Krenzke, Van de Kerckhove,
    and Mohadjer (2005) <http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf>
    and Lohr and Riddles (2016) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR>
    provide an overview of the methods implemented in this package.

Provides a function designed to estimate the minimal sample size required to attain a specific statistical power in the context of linear regression and logistic regression models through simulations.

Implements methods in Mathur and VanderWeele (in preparation) to characterize global evidence strength across W correlated ordinary least squares (OLS) hypothesis tests. Specifically, uses resampling to estimate a null interval for the total number of rejections in, for example, 95% of samples generated with no associations (the global null), the excess hits (the difference between the observed number of rejections and the upper limit of the null interval), and a test of the global null based on the number of rejections.