R package mnormt: The multivariate normal and t distributions , This package provides functions for computing the density and the distribution function of multivariate normal and multivariate ”t” variates, and for generating random vectors sampled from these distributions. Probabilities are computed via a non-Monte Carlo method; different routines are used for the case d=1, d=2, d>2, if d denotes the number of dimensions. (Source:

References in zbMATH (referenced in 12 articles )

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  1. Jian Ma: copent: Estimating Copula Entropy in R (2020) arXiv
  2. Dang, Xin; Sang, Hailin; Weatherall, Lauren: Gini covariance matrix and its affine equivariant version (2019)
  3. Hirk, Rainer; Hornik, Kurt; Vana, Laura: Multivariate ordinal regression models: an analysis of corporate credit ratings (2019)
  4. Picheny, Victor; Binois, Mickael; Habbal, Abderrahmane: A Bayesian optimization approach to find Nash equilibria (2019)
  5. Parisi, Antonio; Liseo, B.: Objective Bayesian analysis for the multivariate skew-(t) model (2018)
  6. Brunero Liseo, Antonio Parisi: Objective Bayesian analysis for the multivariate skew-t model (2017) arXiv
  7. Härdle, Karl Wolfgang; Okhrin, Ostap; Okhrin, Yarema: Basic elements of computational statistics (2017)
  8. Barrett, Jessica; Diggle, Peter; Henderson, Robin; Taylor-Robinson, David: Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference (2015)
  9. Salehi, Mahdi; Doostparast, Mahdi: Expressions for moments of order statistics and records from the skew-normal distribution in terms of multivariate normal orthant probabilities (2015)
  10. Zhu, Xuwen; Melnykov, Volodymyr: Probabilistic assessment of model-based clustering (2015)
  11. Arnošt Komárek; Lenka Komárková: Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data (2014) not zbMATH
  12. Robert, Christian P.; Casella, George: Introducing Monte Carlo methods with R. (2010)