Estimation of observation-specific weights for incomplete longitudinal data and bootstrap procedure for weighted quantile regressions. See Jacqmin-Gadda, Rouanet, Mba, Philipps, Dartigues (2020) for details <doi:10.1177/0962280220909986>.

Estimates the Vevea and Hedges (1995)
    weight-function model. By specifying arguments, users can
    also estimate the modified model described in Vevea and Woods (2005), 
    which may be more practical with small datasets. Users 
    can also specify moderators to estimate a linear model. 
    The package functionality allows users to easily extract the 
    results of these analyses as R objects for other uses. In addition, 
    the package includes a function to launch both models as 
    a Shiny application. Although the Shiny application is also available online, 
    this function allows users to launch it locally if they choose.

Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests.  Also now includes some software for quickly recoding survey data and plotting estimates from interaction terms in regressions (and multiply imputed regressions) both with and without weights. NOTE: Weighted partial correlation calculations pulled to address a bug.

Functions for subject/instance weighted support vector machines (SVM). 
    It uses a modified version of 'libsvm' and is compatible with package 'e1071'. It also allows user defined kernel matrix.

All functions and data sets required for the examples in the book
    Hyndman (2024) "That's Weird: Anomaly Detection Using R" <https://OTexts.com/weird/>.  
    All packages needed to run the examples are also loaded.

Provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.

Implementation of Johansen's general formulation of Welch-James's statistic with Approximate Degrees of Freedom, which makes it suitable for testing 
    any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Some 
    improvements, namely trimmed means and Winsorized variances, and bootstrapping for calculating an empirical critical value, have been added to the classical formulation. 
    The code departs from a previous SAS implementation by L.M. Lix and H.J. Keselman, available at <http://supp.apa.org/psycarticles/supplemental/met_13_2_110/SAS_Program.pdf> and
    published in Keselman, H.J., Wilcox, R.R., and Lix, L.M. (2003) <DOI:10.1111/1469-8986.00060>.