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软件提供了基于协变量非平稳的边际与条件极值模型估计及环境设计轮廓计算功能。采用分段常数表示法描述广义帕累托（GP）超阈值边际模型中阈值和尺度参数在（潜在多维）协变量域上的变化。针对单个条件变量取大值时的一个或多个关联变量的条件变化，采用Heffernan与Tawn（2004）的条件极值模型进行描述，其中斜率项同样假定随协变量呈分段常数变化。通过交叉验证的粗糙度惩罚最大似然估计，确定边际与条件极值模型参数随协变量的最优平滑度。采用自助重抽样方案，量化因边际与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。通过拟合模型下的模拟或数值积分，计算采用包括Huseby等（2013）直接抽样法在内的多种方案的环境轮廓估计。该软件基于MATLAB开发，专为海洋气象工程应用而设计，但可广泛适用于多元超阈值峰值样本。软件及案例数据可从GitHub下载，并附有配套用户指南。