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Papers related to the package | Supplementary materials and vignettes | Density estimation via normal mixtures (and MCMC) | Multivariate GLMM's with a normal mixture in the distribution of random effects | Supporting functions to further process or visualize results from running NMixMCMC or GLMM_MCMC functions | Univariate distributions | Multivariate distributions | Miscellaneous random number generation | Matrix linear algebra | Basic statistics | Utilities | Datasets |

This package contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures.

This overview provides a sorted list of the functions of the package and links to few more involved examples. Methods implemented in the package could be classified into following areas.

• Density estimation via normal mixtures (and MCMC)
• Multivariate generalized linear mixed models with a normal mixture in the distribution of random effects
• Supporting functions to further process or visualize results from running NMixMCMC or GLMM_MCMC functions
• Univariate distributions
• Multivariate distributions
• Miscellaneous random number generation
• Matrix linear algebra
• Basic statistics
• Utilities
• Datasets

Papers related to the package

Please, cite either of the following papers if you find the package useful.

• Komárek, A. (2009). A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data. Computational Statistics and Data Analysis, 53(12), 3932–3947, doi: 10.1016/j.csda.2009.05.006.
  
  
  • Paper describing the methods related to density estimation via normal mixtures and MCMC.
  
  
  
• Komárek, A., Hansen, B. E., Kuiper, E. M. M., van Buuren, H. R., and Lesaffre, E. (2010). Discriminant analysis using a multivariate linear mixed model with a normal mixture in the random effects distribution. Statistics in Medicine, 29(30), 3267–3283, doi: 10.1002/sim.3849.
  
  
  • Paper describing the methods related to discriminant analysis for multivariate longitudinal or grouped data continuous data based on multivariate linear mixed models with a normal mixture in the distribution of random effects.
  
  
  
• Komárek, A. and Komárková, L. (2013). Clustering for multivariate continuous and discrete longitudinal data. The Annals of Applied Statistics, 7(1), 177–200, doi: 10.1214/12-AOAS580.
  
  
  • Paper describing the methods related to cluster analysis for multivariate longitudinal or grouped data continuous and discrete data based on multivariate generalized linear mixed models with a normal mixture in the distribution of random effects.
  
  
  
• Komárek, A. and Komárková, L. (2014). Capabilities of R package mixAK for clustering based on multivariate continuous and discrete longitudinal data. Journal of Statistical Software, accepted for publication.
  
  

Supplementary materials and vignettes

• Komarek-NMix-supplement.pdf
  
  Supplement to Komárek (2009) which includes some technical details not included in the main paper.
  
   
• Galaxy.pdf,  Galaxy.R 
  
  R example of 1-dimensional density estimation using the RJ-MCMC described in Section 6.1 of Komárek (2009).
  
   
• Faithful.pdf,  Faithful.R 
  
  R example of 2-dimensional density estimation described in Section 6.2 of Komárek (2009).
  
   
• Tandmob.pdf,  Tandmob.R 
  
  R example of 6-dimensional density estimation with interval-censored data described in Section 6.3 of Komárek (2009).
  
   
• PBCseq.pdf,  PBCseq.R 
  
  This is an extended version of a paper Komárek and Komárková (2014) and the related R code.
  
   

1. Density estimation via normal mixtures (and MCMC)

2. Multivariate generalized linear mixed models with a normal mixture in the distribution of random effects

3. Supporting functions to further process or visualize results from running NMixMCMC or GLMM_MCMC functions

The following functions are generic with default method and a method for objects of class NMixMCMC and GLMM_MCMC implemented.

Additionally, the following functions are available.

4. Univariate distributions

5. Multivariate distributions

6. Miscellaneous random number generation

7. Matrix linear algebra

8. Basic statistics

9. Utilities

10. Datasets