This paper proposes an algorithm to generate random numbers from any member of the truncated multivariate elliptical family of distributions with a strictly decreasing density generating function. Based on Neal (2003) and Ho et al. (2012), we construct an efficient sampling method by means of a slice sampling algorithm with Gibbs sampler steps. We also provide a faster approach to approximate the first and the second moment for the truncated multivariate elliptical distributions where Monte Carlo integration is used for the truncated partition, and explicit expressions for the non-truncated part (Galarza et al., 2020). Examples and an application to environmental spatial data illustrate its usefulness. Methods are available for free in the new R library elliptical.

翻译：本文提出一种算法,以便从缺电多变椭圆分布系中的任何成员中得出随机数字,其密度生成功能将严格减少。根据Neal(2003年)和Ho等人(2012年),我们通过使用Gibbs采样器步骤的切片抽样算法构建了高效的取样方法。我们还提供了一种更快的方法,以近似截流多变椭圆分布的第一和第二时刻,在断线分割中使用蒙特卡洛融合,以及非断线部分的清晰表达方式(Galarza等人,2020年)。实例和环境空间数据应用说明其有用性。新R图书馆的利普蒂可免费提供方法。