General functions for performing extreme value analysis on a circular 
    domain as part of the statistical methodology in the paper by Konzen, E., 
    Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667.

Calculating silhouette information for clusters on
 circular or linear data using fast algorithms. These algorithms run in
 linear time on sorted data, in contrast to quadratic time by the
 definition of silhouette. When used together with the fast and optimal
 circular clustering method FOCC (Debnath & Song 2021)
 <doi:10.1109/TCBB.2021.3077573> implemented in R package 'OptCirClust',
 circular silhouette can be maximized to find the optimal number of
 circular clusters; it can also be used to estimate the period of noisy
 periodical data.

Circumplex models, which organize constructs in a circle around two 
    underlying dimensions, are popular for studying interpersonal functioning, 
    mood/affect, and vocational preferences/environments. This package provides 
    tools for analyzing and visualizing circular data, including scoring 
    functions for relevant instruments and a generalization of the bootstrapped 
    structural summary method from Zimmermann & Wright (2017) 
    <doi:10.1177/1073191115621795> and functions for creating publication-ready 
    tables and figures from the results.

Routines to fit generalized linear models with constrained coefficients, along with inference on the coefficients. Designed to be used in conjunction with the base glm() function.

Jointly model the accuracy of cognitive responses and item choices
    within a Bayesian hierarchical framework as described by Culpepper and
    Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package
    contains the datasets used within the analysis of the paper.

An implementation of double generalized linear model (DGLM) building with variable selection procedures and handling of interaction terms and other complex situations. We also provide a method of handling convergence issues within the dglm() function. The package offers a simulation function for generating simulated data for testing purposes and utilizes the forward stepwise variable selection procedure in model-building. It also provides a new custom bootstrap function for mean and standard deviation estimation and functions for building crossplots and squareplots from a data set.