The covXtreme software provides functionality for estimation of marginal and conditional extreme value models, non-stationary with respect to covariates, and environmental design contours. Generalised Pareto (GP) marginal models of peaks over threshold are estimated, using a piecewise-constant representation for the variation of GP threshold and scale parameters on the (potentially multidimensional) covariate domain of interest. The conditional variation of one or more associated variates, given a large value of a single conditioning variate, is described using the conditional extremes model of Heffernan and Tawn (2004), the slope term of which is also assumed to vary in a piecewise constant manner with covariates. Optimal smoothness of marginal and conditional extreme value model parameters with respect to covariates is estimated using cross-validated roughness-penalised maximum likelihood estimation. Uncertainties in model parameter estimates due to marginal and conditional extreme value threshold choice, and sample size, are quantified using a bootstrap resampling scheme. Estimates of environmental contours using various schemes, including the direct sampling approach of Huseby et al. 2013, are calculated by simulation or numerical integration under fitted models. The software was developed in MATLAB for metocean applications, but is applicable generally to multivariate samples of peaks over threshold. The software and case study data can be downloaded from GitHub, with an accompanying user guide.

翻译：covXtreme软件提供了估计边缘与条件极值模型（随协变量非平稳变化）以及环境设计轮廓的功能。采用分段常数表示法在（潜在多维）协变量域上刻画广义帕累托（GP）超阈值模型的阈值和尺度参数变化，从而估计峰值超阈值（POT）的GP边缘模型。基于Heffernan和Tawn（2004）的条件极值模型，描述给定单个条件变量大值时一个或多个关联变量的条件变化，其中斜率项也假设随协变量呈分段常数变化。通过交叉验证的粗糙度惩罚极大似然估计，估计边缘和条件极值模型参数关于协变量的最优平滑度。采用自助重抽样方案，量化由边缘和条件极值阈值选择及样本量引起的模型参数估计不确定性。在拟合模型下通过模拟或数值积分，计算包括Huseby等人（2013）直接采样法在内的多种环境轮廓估计。该软件基于MATLAB开发，主要用于海洋气象应用，但普遍适用于多元超阈值峰值样本。软件及案例研究数据可从GitHub下载并附有配套用户指南。