Last data update: 2014.03.03

## Data Source

R Release (3.2.3)
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## Classification

Results 1 - 8 of 8 found.
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### mvn.ub (Package: miscF) : Unbiased Estimate of Parameters of a Multivariate Normal Distribution

Obtain the Unbiased Estimate of Parameters of a Multivariate Normal Distribution.
● Data Source: CranContrib
● Keywords: multivariate
● Alias: mvn.ub
● 0 images

### mvt.mcmc (Package: miscF) : Estimate Parameters of a Multivariate t Distribution Using the MCMC

Use the MCMC to obtain estimate of parameters of a multivariate t distribution.
● Data Source: CranContrib
● Keywords: multivariate
● Alias: mvt.mcmc
● 0 images

### rMultinom (Package: miscF) : Generate Random Samples from Different Multinomial

Generate random samples from multinomial distributions with the same number of classes but different event probabilities.
● Data Source: CranContrib
● Keywords: distribution
● Alias: rMultinom
● 0 images

### uvnm.rjmcmc (Package: miscF) : Univariate Normal Mixture (UVNM) Model with Unknown Number of Components

Estimate the parameters of an univariate normal mixture model including the number of components using the Reversible Jump MCMC method. It can be used for density estimation and/or classification.
● Data Source: CranContrib
● Keywords: distribution
● Alias: uvnm.rjmcmc
● 0 images

### mvt.ecme (Package: miscF) : Estimate Parameters of a Multivariate t Distribution Using the

Use the Expectation/Conditional Maximization Either (ECME) algorithm to obtain estimate of parameters of a multivariate t distribution.
● Data Source: CranContrib
● Keywords: multivariate
● Alias: mvt.ecme
● 0 images

### curve.polynomial.rjmcmc (Package: miscF) : Curve Fitting Using Piecewise Polynomials with Unknown Number and

Fit a variety of curves by a sequence of piecewise polynomials. The number and location of knots are determined by the Reversible Jump MCMC method.
● Data Source: CranContrib
● Keywords: smooth
● Alias: curve.polynomial.rjmcmc
● 0 images

### spatail.lme.mcmc (Package: miscF) : Spatial Modeling by a Bayesian Hierarchical

A linear mixed-effects model that combines unstructured variance/covariance matrix for inter-regional (long-range) correlations and an exchangeable correlation structure for intra-regional (short-range) correlations. Estimation is performed using the Gibbs sampling.
● Data Source: CranContrib
● Keywords: spatial
● Alias: spatial.lme.mcmc
● 0 images

### mvn.bayes (Package: miscF) : Estimate the Parameters of a Multivariate Normal Model by the

Estimate the parameters of a multivariate normal model under different priors.
● Data Source: CranContrib
● Keywords: multivariate
● Alias: mvn.bayes
● 0 images