Package: mixAK 5.8

Arnošt Komárek
mixAK: Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering
Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models. The package is primarily related to the publications Komárek (2009, Comp. Stat. and Data Anal.) <doi:10.1016/j.csda.2009.05.006> and Komárek and Komárková (2014, J. of Stat. Soft.) <doi:10.18637/jss.v059.i12>. It also implements methods published in Komárek and Komárková (2013, Ann. of Appl. Stat.) <doi:10.1214/12-AOAS580>, Hughes, Komárek, Bonnett, Czanner, García-Fiñana (2017, Stat. in Med.) <doi:10.1002/sim.7397>, Jaspers, Komárek, Aerts (2018, Biom. J.) <doi:10.1002/bimj.201600253> and Hughes, Komárek, Czanner, García-Fiñana (2018, Stat. Meth. in Med. Res) <doi:10.1177/0962280216674496>.
Authors:
mixAK_5.8.tar.gz
mixAK_5.8.tar.gz(r-4.5-noble)mixAK_5.8.tar.gz(r-4.4-noble)
mixAK_5.8.tgz(r-4.4-emscripten)mixAK_5.8.tgz(r-4.3-emscripten)
mixAK.pdf |mixAK.html✨
mixAK/json (API)
NEWS
# Install 'mixAK' in R: |
install.packages('mixAK', repos = 'https://cloud.r-project.org') |
- Acidity - Acidity index of lakes in North-Central Wisconsin
- Enzyme - Enzymatic activity in the blood
- Faithful - Old Faithful Geyser Data
- Galaxy - Velocities of distant galaxies
- PBC910 - Subset of Mayo Clinic Primary Biliary Cholangitis (Cirrhosis) data
- PBCseq - Mayo Clinic Primary Biliary Cholangitis (Cirrhosis), sequential data
- SimData - Simulated dataset
- Tandmob - Signal Tandmobiel data
- TandmobEmer - Signal Tandmobiel data - emergence times
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 months agofrom:a50c3065b9. Checks:3 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 16 2025 |
R-4.5-linux-x86_64 | OK | Mar 16 2025 |
R-4.4-linux-x86_64 | OK | Mar 16 2025 |
Exports:autolayoutBLABsBasisC_GLMM_MCMCC_GLMM_NMixRelabelC_NMix_ChainsDerivedC_NMix_MCMCC_NMix_NMixRelabelC_NMix_PEDC_NMix_PredCDFMargC_NMix_PredCondDensCDFMargC_NMix_PredCondDensJoint2C_NMix_PredDAC_NMix_PredDensJoint2C_NMix_PredDensMargcbplotdMVNdMVNmixturedMVNmixture2dMVTdWISHARTfitted.GLMM_MCMCgeneratePermutationsgetProfilesGLMM_longitDAGLMM_longitDA2GLMM_MCMCGLMM_MCMCdataGLMM_MCMCifitGLMM_MCMCinit.alphaGLMM_MCMCinit.bGLMM_MCMCinit.epsGLMM_MCMCprior.alphaGLMM_MCMCprior.bGLMM_MCMCprior.epsGLMM_MCMCscale.bGLMM_MCMCwrapperMatMPpinvMatSqrtNMixChainCompNMixChainComp.defaultNMixChainComp.GLMM_MCMCNMixChainComp.NMixMCMCNMixChainsDerivedNMixClusterNMixCluster.defaultNMixCluster.GLMM_MCMCNMixEMNMixMCMCNMixMCMCdataNMixMCMCinitrNMixMCMCinityNMixMCMCwrapperNMixPlugCondDensJoint2NMixPlugCondDensJoint2.defaultNMixPlugCondDensJoint2.GLMM_MCMCNMixPlugCondDensJoint2.NMixMCMCNMixPlugCondDensMargNMixPlugCondDensMarg.defaultNMixPlugCondDensMarg.GLMM_MCMCNMixPlugCondDensMarg.NMixMCMCNMixPlugDANMixPlugDensJoint2NMixPlugDensJoint2.defaultNMixPlugDensJoint2.GLMM_MCMCNMixPlugDensJoint2.NMixMCMCNMixPlugDensMargNMixPlugDensMarg.defaultNMixPlugDensMarg.GLMM_MCMCNMixPlugDensMarg.NMixMCMCNMixPredCDFMargNMixPredCDFMarg.defaultNMixPredCDFMarg.GLMM_MCMCNMixPredCDFMarg.NMixMCMCNMixPredCondCDFMargNMixPredCondCDFMarg.defaultNMixPredCondCDFMarg.GLMM_MCMCNMixPredCondCDFMarg.NMixMCMCNMixPredCondDensJoint2NMixPredCondDensJoint2.defaultNMixPredCondDensJoint2.GLMM_MCMCNMixPredCondDensJoint2.NMixMCMCNMixPredCondDensMargNMixPredCondDensMarg.defaultNMixPredCondDensMarg.GLMM_MCMCNMixPredCondDensMarg.NMixMCMCNMixPredDANMixPredDensJoint2NMixPredDensJoint2.defaultNMixPredDensJoint2.GLMM_MCMCNMixPredDensJoint2.NMixMCMCNMixPredDensMargNMixPredDensMarg.defaultNMixPredDensMarg.GLMM_MCMCNMixPredDensMarg.NMixMCMCNMixPseudoGOFNMixPseudoGOF.defaultNMixPseudoGOF.NMixMCMCNMixRelabelNMixRelabel.defaultNMixRelabel.GLMM_MCMCNMixRelabel.GLMM_MCMClistNMixRelabel.NMixMCMCNMixRelabel.NMixMCMClistNMixRelabelAlgorithmNMixSummCompNMixSummComp.defaultNMixSummComp.GLMM_MCMCNMixSummComp.NMixMCMCplot.NMixPlugCondDensJoint2plot.NMixPlugCondDensMargplot.NMixPlugDensJoint2plot.NMixPlugDensMargplot.NMixPredCDFMargplot.NMixPredCondCDFMargplot.NMixPredCondDensJoint2plot.NMixPredCondDensMargplot.NMixPredDensJoint2plot.NMixPredDensMargplotProfilesprint.GLMM_MCMCprint.GLMM_MCMClistprint.NMixEMprint.NMixMCMCprint.NMixMCMClistrcMVNrDirichletrMVNrMVNmixturerMVNmixture2rMVTrRotationMatrixrSamplePairrTMVNrTNormrWISHARTSP2RectsummaryDifftracePlotstracePlots.GLMM_MCMCtracePlots.GLMM_MCMClisttracePlots.NMixMCMCtracePlots.NMixMCMClistY2TY2T.NMixPlugCondDensJoint2Y2T.NMixPlugCondDensMargY2T.NMixPlugDensJoint2Y2T.NMixPlugDensMargY2T.NMixPredCDFMargY2T.NMixPredCondCDFMargY2T.NMixPredCondDensJoint2Y2T.NMixPredCondDensMargY2T.NMixPredDensJoint2Y2T.NMixPredDensMarg
Dependencies:bootcodacolorspacefastGHQuadlatticelme4MASSMatrixminqamnormtnlmenloptrrbibutilsRcppRcppEigenRdpackreformulas
Citation
To cite the package mixAK in publications use either of the following articles:
Komárek A, Komárková L (2014). “Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data.” Journal of Statistical Software, 59(12), 1–38. doi:10.18637/jss.v059.i12.
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 & Data Analysis, 53(12), 3932–3947. doi:10.1016/j.csda.2009.05.006.
Corresponding BibTeX entries:
@Article{, title = {Capabilities of {R} Package {mixAK} for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data}, author = {Arnošt Komárek and Lenka Komárková}, journal = {Journal of Statistical Software}, year = {2014}, volume = {59}, number = {12}, pages = {1--38}, doi = {10.18637/jss.v059.i12}, }
@Article{, title = {A New {R} Package for {B}ayesian Estimation of Multivariate Normal Mixtures Allowing for Selection of the Number of Components and Interval-Censored Data}, author = {Arnošt Komárek}, journal = {Computational Statistics \& Data Analysis}, year = {2009}, volume = {53}, number = {12}, pages = {3932--3947}, doi = {10.1016/j.csda.2009.05.006}, }