Title: Mixed Tempered Stable Distribution
Description: This package provides detailed functions for univariate Mixed Tempered Stable distribution distribution with Gamma density. This distribution encompasses, Variance Gamma and Symmetric Geo-Stable as special cases. The package contains routine for mle estimation, for the computation of density, probability, quantile and random numbers

Depends: methods, stats, graphics, stats4, MASS
Description: We provide detailed functions for univariate Mixed Tempered Stable distribution.

person("Dan", "Nettleton", role = "ctb"))
Imports: MASS, Matrix, RColorBrewer, fftw, MixSim, EMCluster
Enhances: pbdMPI (>= 0.3-4), AnalyzeFMRI, oro.nifti
Description: Utilizing model-based clustering (unsupervised)
for functional magnetic resonance imaging (fMRI) data.
The developed methods (Chen and Maitra (2018, manuscript)) include
2D and 3D clustering analyses (for p-values with voxel locations) and
segmentation analyses (for p-values alone) for fMRI data where p-values
indicate significant level of activation responding to stimulate
of interesting. The analyses are mainly identifying active
voxel/signal associated with normal brain behaviors.
Analysis pipelines (R scripts) utilizing this package
(see examples in 'inst/workflow/') is also implemented with high

The developed methods (Chen and Maitra (2021) <arXiv:2102.03639>)

Title: Inference for Finite Mixture Models
Description: Likelihood-based methods for inference for finite mixture

Author: Shaoting Li, Jiahua Chen and Pengfei Li.
Description: Functions for computing the penalized maximum likelihood estimate (PMLE) or maximum likelihood estimate (MLE), testing the order of a finite mixture model using EM-test, drawing histogram of observations and the fitted density or probability mass function of the mixture model.
Packaged: 2016-03-21 22:22:03 UTC; Administrator

Title: Robust Model-Based Clustering for Data Sets with Missing Values
Description: Implementation of robust model based cluster analysis with missing data.
The models used are: Multivariate Contaminated Normal Mixtures (MCNM),
Multivariate Student's t  Mixtures (MtM), and Multivariate Normal Mixtures (MNM)
for data sets with missing values at random.
"Cluster analysis and outlier detection with missing data"
Hung Tong, Cristina Tortora (2020) <arXiv:2012.05394>.
Imports: ContaminatedMixt (>= 1.3.4.1), mvtnorm (>= 1.1-2), mnormt (>=