ralphma1203.r-universe.dev

[cran] tmvmixnorm 1.2.0

tingfung@mailbox.sc.edu (Ting Fung Ma) — Mon, 11 May 2026 08:40:13 GMT

Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong. Algorithms proposed by Li and Ghosh (2015) are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multivariate Student's t distribution is also developed. Moreover, the proposed method and computation remain valid for high dimensional and strong dependence scenarios. Empirical results in Li and Ghosh (2015) illustrated the superior performance in terms of various criteria (e.g. mixing and integrated auto-correlation time).

[ralphma1203] tmvmixnorm 1.2.0

tingfung@mailbox.sc.edu (Ting Fung Ma) — Mon, 11 May 2026 08:40:13 GMT

[ralphma1203] clordr 1.7.2

tingfung@mailbox.sc.edu (Ting Fung Ma) — Sat, 09 May 2026 18:09:55 GMT

Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error. Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) , can act as a useful tool for model diagnostics.