Showing 7 of total 7 results (show query)
modal-inria
RMixtComp:Mixture Models with Heterogeneous and (Partially) Missing Data
Mixture Composer (Biernacki (2015) <https://inria.hal.science/hal-01253393v1>) is a project to perform clustering using mixture models with heterogeneous data and partially missing data. Mixture models are fitted using a SEM algorithm. It includes 8 models for real, categorical, counting, functional and ranking data.
Maintained by Quentin Grimonprez. Last updated 11 months ago.
clusteringcppheterogeneous-datamissing-datamixed-datamixture-modelstatistics
13 stars 6.10 score 12 scripts
andybega
spduration:Split-Population Duration (Cure) Regression
An implementation of split-population duration regression models. Unlike regular duration models, split-population duration models are mixture models that accommodate the presence of a sub-population that is not at risk for failure, e.g. cancer patients who have been cured by treatment. This package implements Weibull and Loglogistic forms for the duration component, and focuses on data with time-varying covariates. These models were originally formulated in Boag (1949) and Berkson and Gage (1952), and extended in Schmidt and Witte (1989).
Maintained by Andreas Beger. Last updated 1 years ago.
mixture-modelregressionsplit-populationsurvival-analysiscpp
4 stars 5.38 score 40 scriptsmodal-inria
RMixtCompUtilities:Utility Functions for 'MixtComp' Outputs
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with heterogeneous data sets and partially missing data management. This package contains graphical, getter and some utility functions to facilitate the analysis of 'MixtComp' output.
Maintained by Quentin Grimonprez. Last updated 11 months ago.
clusteringcppheterogeneous-datamissing-datamixed-datamixture-modelstatistics
13 stars 5.19 score 2 scripts 1 dependents
cbg-ethz
clustNet:Network-Based Clustering
Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.
Maintained by Fritz Bayer. Last updated 1 years ago.
bayesian-networkbayesian-networksclusteringdaggenomicsmixture-modelnetwork-clustering
7 stars 5.16 score 41 scripts
pletschm
aldvmm:Adjusted Limited Dependent Variable Mixture Models
The goal of the package 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using normal component distributions and a multinomial logit model of probabilities of component membership.
Maintained by Mark Pletscher. Last updated 1 years ago.
clinical-trialscost-effectivenesseq5dfinite-mixturehealth-economicshtahuilimited-dependent-variablemappingmixture-modelpatient-reported-outcomesquality-of-lifeutilities
4 stars 4.30 score 2 scripts
suren-rathnayake
EMMIXmfa:Mixture Models with Component-Wise Factor Analyzers
We provide functions to fit finite mixtures of multivariate normal or t-distributions to data with various factor analytic structures adopted for the covariance/scale matrices. The factor analytic structures available include mixtures of factor analyzers and mixtures of common factor analyzers. The latter approach is so termed because the matrix of factor loadings is common to components before the component-specific rotation of the component factors to make them white noise. Note that the component-factor loadings are not common after this rotation. Maximum likelihood estimators of model parameters are obtained via the Expectation-Maximization algorithm. See descriptions of the algorithms used in McLachlan GJ, Peel D (2000) <doi:10.1002/0471721182.ch8> McLachlan GJ, Peel D (2000) <ISBN:1-55860-707-2> McLachlan GJ, Peel D, Bean RW (2003) <doi:10.1016/S0167-9473(02)00183-4> McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015> Baek J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149> Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112> McLachlan GJ, Baek J, Rathnayake SI (2011) <doi:10.1002/9781119995678.ch9>.
Maintained by Suren Rathnayake. Last updated 6 years ago.
clusteringclustering-algorithmmixture-distributionsmixture-model
3 stars 3.29 score 13 scripts
ardiad
AdMit:Adaptive Mixture of Student-t Distributions
Provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself.
Maintained by David Ardia. Last updated 3 years ago.
adaptivedistributionfittingmcmcmixturemixture-model
2 stars 3.00 score 9 scripts