The usual distribution functions, maximum likelihood inference and
    model diagnostics for univariate stationary extreme value mixture models
    are provided. Kernel density estimation including various boundary
    corrected kernel density estimation methods and a wide choice of kernels,
    with cross-validation likelihood based bandwidth estimator.
    Reasonable consistency with the base functions in the 'evd' package is
    provided, so that users can safely interchange most code.

Tools for extreme value modeling based on the r-largest 
    order statistics framework. The package provides functions for 
    parameter estimation via maximum likelihood, return level 
    estimation with standard errors, profile likelihood-based 
    confidence intervals, random sample generation, and entropy 
    difference tests for selecting the number of order statistics r. 
    Several r-largest order statistics models are implemented, 
    including the four-parameter kappa (rK4D), generalized logistic 
    (rGLO), generalized Gumbel (rGGD), logistic (rLD), and Gumbel 
    (rGD) distributions. The rK4D methodology is described in 
    Shin et al. (2022) <doi:10.1016/j.wace.2022.100533>, the rGLO 
    model in Shin and Park (2024) <doi:10.1007/s00477-023-02642-7>, 
    and the rGGD model in Shin and Park (2025) 
    <doi:10.1038/s41598-024-83273-y>. The underlying distributions 
    are related to the kappa distribution of Hosking (1994) 
    <doi:10.1017/CBO9780511529443>, the generalized logistic 
    distribution discussed by Ahmad et al. (1988) 
    <doi:10.1016/0022-1694(88)90015-7>, and the generalized Gumbel 
    distribution of Jeong et al. (2014) 
    <doi:10.1007/s00477-014-0865-8>. Penalized likelihood approaches 
    for extreme value estimation follow Martins and Stedinger (2000) 
    <doi:10.1029/1999WR900330> and Coles and Dixon (1999) 
    <doi:10.1023/A:1009905222644>. Selection of r is supported using 
    methods discussed in Bader et al. (2017) 
    <doi:10.1007/s11222-016-9697-3>. The package is intended for 
    hydrological, climatological, and environmental extreme value 
    analysis.