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mixbox (version 1.2.3)

Observed Fisher Information Matrix for Finite Mixture Model

Description

Developed for the following tasks. 1- simulating realizations from the canonical, restricted, and unrestricted finite mixture models. 2- Monte Carlo approximation for density function of the finite mixture models. 3- Monte Carlo approximation for the observed Fisher information matrix, asymptotic standard error, and the corresponding confidence intervals for parameters of the mixture models sing the method proposed by Basford et al. (1997) .

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Version

Install

install.packages('mixbox')

Monthly Downloads

176

Version

1.2.3

License

GPL (>= 2)

Maintainer

Mahdi Teimouri

Last Published

February 25th, 2024

Functions in mixbox (1.2.3)

Generating from multivariate normal distribution with location vector \(\bold{\mu}\) and covariance matrix \(\Sigma_{d \times d}\).

Generating realization from finite mixture models.

Creating name for columns and rows of OFI matrix.

Simulating from polynomially tilted \(\alpha\)-stable (ptstable) random variable.

Computing posteriors expected value.

Computing observed Fisher information matrix for unrestricted or canonical finite mixture model.

Approximating the asymptotic standard error for parameters of the finite mixture models based on the observed Fisher information matrix.

Simulating from inversse Gaussian random variable.

Computing observed Fisher information matrix for restricted finite mixture model.

Output configuration.

Approximating the density function of the finite mixture models applied for model-based clustering.

Extracting diagonal and upper off-diagonal elements of a squre matrix.

Extracting diagonal and upper off-diagonal elements of a squre matrix.

Monte Carlo approximation for density function of positive alpha-stable distribution.

Computing posteriors expected value.

Output configuration.

Monte Carlo approximation for density function of polynomially tilted alpha-stable distribution.

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