Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.

Weighted generalized estimating equations (WGEE) is an extension of generalized linear models to longitudinal clustered data by incorporating the correlation within-cluster when data is missing at random (MAR). The parameters in mean, scale correlation structures are estimated based on quasi-likelihood. Multiple model selection criterion are provided for selection of mean model and working correlation structure based on WGEE/GEE.

Functions for the analysis of whole-genome sequencing studies to simultaneously detect the existence, and estimate the locations of association signals at genome-wide scale. The functions allow genome-wide association scan, candidate region scan and single window test.

Functions for determining the effect of data weights on the variance of survey data: users will load a data set which has a weights column, and the package will calculate the design effect (DEFF), weighting loss, root design effect (DEFT), effective sample size (ESS), and/or weighted margin of error.

An enhanced implementation of Whittaker-Henderson smoothing for the gradation 
    of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks.
    'WH' is based on the methods described in Biessy (2023) <doi:10.48550/arXiv.2306.06932>.
    Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, 
    automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.

Inferences about counterfactuals are essential for prediction,
      answering what if questions, and estimating causal effects.
      However, when the counterfactuals posed are too far from the data at
      hand, conclusions drawn from well-specified statistical analyses
      become based largely on speculation hidden in convenient modeling
      assumptions that few would be willing to defend. Unfortunately,
      standard statistical approaches assume the veracity of the model
      rather than revealing the degree of model-dependence, which makes this
      problem hard to detect. WhatIf offers easy-to-apply methods to
      evaluate counterfactuals that do not require sensitivity testing over
      specified classes of models. If an analysis fails the tests offered
      here, then we know that substantive inferences will be sensitive to at
      least some modeling choices that are not based on empirical evidence,
      no matter what method of inference one chooses to use. WhatIf
      implements the methods for evaluating counterfactuals discussed in
      Gary King and Langche Zeng, 2006, "The Dangers of Extreme
      Counterfactuals," Political Analysis 14 (2) <DOI:10.1093/pan/mpj004>; 
      and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The 
      Pitfalls of Counterfactual Inference," International Studies 
      Quarterly 51 (March) <DOI:10.1111/j.1468-2478.2007.00445.x>.