FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters

By Bettina Grün, Friedrich Leisch. 2008

489 citations recorded by the source corpus.

flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectation-maximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can be fitted. The application of the package is demonstrated on several examples, the implementation described and examples given to illustrate how new drivers for the component specific models and the concomitant variable models can be defined.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e15adb189e1aff8b6f80a1fed5ca34981181fb51

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Citations in the corpus

mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models (2016, methodology): “We also included ﬂexmix (Leisch, 2004; Grün and Leisch, 2007, 2008) which provides a general framework for ﬁnite mixtures of regression models using the EM algorithm, since it can be adapted to perform Gaussian model-based clustering using a limited set of models (only the diagonal and…”
glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling (2017, methodology): “Several other packages have similar capabilities for ﬁtting zero-inﬂated GLMs ( ﬂexmix , MXM , VGAM : Grün and Leisch, 2008; Lagani et al., 2017; Yee, 2017), but in this paper we focus on packages that can also estimate random effects.”
Estimation of extended mixed models using latent classes and latent processes: the R package lcmm (2015, methodology): “multivariate longitudinal data based on MCMC estimation; package FlexMix (Leisch 2004; Grün and Leisch 2008) also proposes the estimation of classes of mixture models including the latent class linear mixed model with function FLXMRlmer; and mixtools (Benaglia, Chauveau, Hunter, and Young 2009) incl”
Visualizing Count Data Regressions Using Rootograms (2016, methodology): “For R users, these data are conveniently available from the AER package supplementing Kleiber and Zeileis (2008) under the name NMES1988.”
Team Faultline Measures (2013, methodology): “…for the open-source statistical environment R (R Development Core Team, 2012) according to the formulas and descriptions provided by the original authors.5 Our implementation of the LCCA approach builds on the FlexMix package (Grün & Leisch, 2007, 2008; Leisch, 2004), which provides LCA in R.”
poLCA: An R Package for Polytomous Variable Latent Class Analysis (2011, methodology): “Package flexmix has the capacity to estimate latent class regression models, but not without considerable extra programming effort on the part of the user (Grün and Leisch 2008).”
Mixtures of Experts Models (2018, methodology): “The flexmix package (Grün & Leisch, 2008b) has model ﬁtting capabilities for a range of mixture of regression models, which include covariates (or concomitant variables), as does the mixreg package (Turner, 2014).”
ℓ1-penalization for mixture regression models (2010, methodology): “The adaptive Lasso in linear models has better variable selection properties than the Lasso, see Zou (2006), Huang et al. (2008), van de Geer et al. (2010). We present some theory for the adaptive estimator in FMR models in Section 4.”
mixtools: An R Package for Analyzing Mixture Models (2009, methodology): “This is the main feature that distinguishes mixtools from other mixture-related R packages, also available from the Comprehensive R Archive Network at http://CRAN.R-project.org/ , such as mclust (Fraley and Raftery 2009) and ﬂexmix (Leisch 2004; Gr¨un and Leisch 2008).”
Modeling loss data using mixtures of distributions (2016, methodology): “The methodology developed in this paper is implemented using the statistical computing environment R and is available in the R package flexmix (Grün and Leisch, 2008).”