tmvtnorm: Truncated Multivariate Normal and Student t Distribution Random number generation for the truncated multivariate normal and Student t distribution. Computes probabilities, quantiles and densities, including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.

References in zbMATH (referenced in 11 articles )

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  1. Theodosiadou, Ourania; Tsaklidis, George: Estimating the positive and negative jumps of asset returns via Kalman filtering. The case of Nasdaq index (2017)
  2. Kim, Hea-Jung; Choi, Taeryon; Jo, Seongil: Bayesian factor analysis with uncertain functional constraints about factor loadings (2016)
  3. Lu, Jiannan; Deng, Alex: Demystifying the bias from selective inference: a revisit to Dawid’s treatment selection problem (2016)
  4. Cui, Shiqi; Guha, Subharup; Ferreira, Marco A. R.; Tegge, Allison N.: HmmSeq: a hidden Markov model for detecting differentially expressed genes from RNA-seq data (2015)
  5. Kim, Hea-Jung: A best linear threshold classification with scale mixture of skew normal populations (2015)
  6. Yue, Chen; Chen, Shaojie; Sair, Haris I.; Airan, Raag; Caffo, Brian S.: Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models (2015)
  7. Emery, Xavier; Arroyo, Daisy; Peláez, María: Simulating large Gaussian random vectors subject to inequality constraints by Gibbs sampling (2014)
  8. Grigorova, Denitsa; Encheva, Elitsa; Gueorguieva, Ralitza: EM algorithm for MLE of a probit model for multiple ordinal outcomes (2013)
  9. Zhang, Zhiyong; Wang, Lijuan: Methods for mediation analysis with missing data (2013)
  10. Manjunath, B. G.; Frick, Melanie; Reiss, Rolf-Dieter: Some notes on extremal discriminant analysis (2012)
  11. Griffiths, William: A Gibbs’ sampler for the parameters of a truncated multivariate normal distribution (2004)