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mnorm: An R Package for Calculation and Differentiation of Conditional Multivariate Normal Densities and Probabilities

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  • Sofiia Dolgikh
  • Bogdan Potanin

Abstract

We introduce the mnorm package, which allows one to calculate conditional multivariate normal densities and probabilities and to differentiate them with respect to various parameters including covariances and integration limits. The package also supports parallel (multi-core) computing, handles non-normal marginals via the Gaussian copula, and provides fast routines for the calculation of bivariate and trivariate normal probabilities. The package is of special interest for the implementation of the maximum-likelihood estimators in econometric models.

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  • Sofiia Dolgikh & Bogdan Potanin, 2026. "mnorm: An R Package for Calculation and Differentiation of Conditional Multivariate Normal Densities and Probabilities," Papers 2607.15382, arXiv.org.
    • Handle: RePEc:arx:papers:2607.15382
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    References listed on IDEAS

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    1. Yan, Jun, 2007. "Enjoy the Joy of Copulas: With a Package copula," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i04).
    2. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.
    3. Enkelejd Hashorva & Jürg Hüsler, 2003. "On multivariate Gaussian tails," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(3), pages 507-522, September.
    4. Sofiia Dolgikh & Bogdan Potanin, 2022. "Estimating the effect of higher education on abortion," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 117-139.
    5. Juri Trifonov & Potanin Trifonov, 2022. "Semi-Nonparametric Generalized Autoregressive Conditional Heteroscedasticity Model with Application to Bitcoin Volatility Estimation," HSE Economic Journal, National Research University Higher School of Economics, vol. 26(4), pages 623-646.
    6. Eddelbuettel, Dirk & Francois, Romain, 2011. "Rcpp: Seamless R and C++ Integration," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i08).
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