FlexMix: A general framework for finite mixture models and latent class regression in R
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TLDR
FlexMix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment and provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models.Abstract:
FlexMix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment: three variants of the EM algorithm can be used for parameter estimation, regressors and responses may be multivariate with arbitrary dimension, data may be grouped, e.g., to account for multiple observations per individual, the usual formula interface of the S language is used for convenient model specification, and a modular concept of driver functions allows to interface many dierent types of regression models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models.read more
Citations
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Regression Models for Count Data in R
TL;DR: In this article, a new implementation of hurdle and zero-inflated regression models in the functions hurdle() and zeroinfl() from the package pscl is introduced, which reuses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models.
Journal ArticleDOI
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models.
TL;DR: This updated version of mclust adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.
Journal ArticleDOI
Beta Regression in R
TL;DR: The betareg package is described which provides the class of beta regressions in the R system for statistical computing and incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions.
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mixtools: An R Package for Analyzing Finite Mixture Models
TL;DR: The mixtools package for R provides a set of functions for analyzing a variety of finite mixture models, which include both traditional methods, such as EM algorithms for univariate and multivariate normal mixtures, and newer methods that reflect some recent research in finite mixture Models.
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poLCA: An R Package for Polytomous Variable Latent Class Analysis
Drew A. Linzer,Jeffrey B. Lewis +1 more
TL;DR: poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment using expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.
References
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Maximum likelihood from incomplete data via the EM algorithm
Book
Finite Mixture Models
Geoffrey J. McLachlan,David Peel +1 more
TL;DR: The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the mathematical and statistical literature.
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Model-Based Clustering, Discriminant Analysis, and Density Estimation
Chris Fraley,Adrian E. Raftery +1 more
TL;DR: This work reviews a general methodology for model-based clustering that provides a principled statistical approach to important practical questions that arise in cluster analysis, such as how many clusters are there, which clustering method should be used, and how should outliers be handled.
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Statistical analysis of finite mixture distributions
TL;DR: This course discusses Mathematical Aspects of Mixtures, Sequential Problems and Procedures, and Applications of Finite Mixture Models.
Book
Market Segmentation: Conceptual and Methodological Foundations
Michel Wedel,Wagner A. Kamakura +1 more
TL;DR: Applied market segmentation: general observable bases - geo-demographics general unobservable bases - values and lifestyles - conjoint analysis conclusions and directions for future research.