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.

Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.

Executes nonstationary Fay-Herriot model and nonstationary generalized linear mixed model for small area estimation.The empirical best linear unbiased predictor (EBLUP) under stationary and nonstationary Fay-Herriot models and empirical best predictor (EBP) under nonstationary generalized linear mixed model along with the mean squared error estimation are included. EBLUP for prediction of non-sample area is also included under both stationary and nonstationary Fay-Herriot models. This extension to the Fay-Herriot model that accounts for the presence  of spatial nonstationarity was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2015) <doi:10.1093/jssam/smu026> and nonstationary generalized linear mixed model was developed by Hukum  Chandra, Nicola Salvati and Ray Chambers (2017) <doi:10.1016/j.spasta.2017.01.004>. This package is dedicated to the memory of Dr. Hukum Chandra who passed away while the package creation was in progress.