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Title:

FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R

Publisher:

University of California at Los Angeles, Department of Statistics

Year of Publication:

2004-01-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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Title:

FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R

Description:

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 i...

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 different 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. Minimize

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article

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Title:

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

Description:

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 ...

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. Minimize

Publisher:

University of California, Los Angeles

Year of Publication:

2008-09-01T00:00:00Z

Source:

Journal of Statistical Software, Vol 28, Iss 4 (2008)

Journal of Statistical Software, Vol 28, Iss 4 (2008) Minimize

Document Type:

article

Language:

English

Subjects:

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC...

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

DDC:

310 Collections of general statistics *(computed)*

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http://www.jstatsoft.org/v28/i04/paper

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Title:

Identifiability of Finite Mixtures of Multinomial Logit Models with Varying and Fixed Effects

Description:

Conditional logit, Finite mixture, Identifiability, Multinomial logit, Unobserved heterogeneity

Conditional logit, Finite mixture, Identifiability, Multinomial logit, Unobserved heterogeneity Minimize

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Title:

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

Description:

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 ...

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. Minimize

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article

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Evaluation of structure and reproducibility of cluster solutions using the bootstrap

Description:

Cluster analysis, Mixture models, Bootstrap

Cluster analysis, Mixture models, Bootstrap Minimize

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article

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Title:

R Version 2.0.0

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article

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Title:

Exploratory analysis of benchmark experiments an interactive approach

Description:

Benchmark experiment, Visualization, Interactive data analysis

Benchmark experiment, Visualization, Interactive data analysis Minimize

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article

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Title:

Sweave: Dynamic generation of statistical reports using literate data analysis

Description:

This paper has been accepted for publication in:

This paper has been accepted for publication in: Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2014-12-12

Source:

http://epub.wu.ac.at/1788/1/document.pdf

http://epub.wu.ac.at/1788/1/document.pdf Minimize

Document Type:

text

Language:

en

Subjects:

S ; literate statistical practice ; integrated statistical documents ; reproducible research

S ; literate statistical practice ; integrated statistical documents ; reproducible research Minimize

Rights:

Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Creating R Packages: A Tutorial

Description:

This is a reprint of an article that has appeared in: Paula Brito, editor, Compstat 2008-Proceedings in Computational Statistics. Physica Verlag, Heidelberg, Germany, 2008. This tutorial gives a practical introduction to creating R packages. We discuss how object oriented programming and S formulas can be used to give R code the usual look and f...

This is a reprint of an article that has appeared in: Paula Brito, editor, Compstat 2008-Proceedings in Computational Statistics. Physica Verlag, Heidelberg, Germany, 2008. This tutorial gives a practical introduction to creating R packages. We discuss how object oriented programming and S formulas can be used to give R code the usual look and feel, how to start a package from a collection of R functions, and how to test the code once the package has been created. As running example we use functions for standard linear regression analysis which are developed from scratch. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-01-15

Source:

http://epub.ub.uni-muenchen.de/6175/2/tr036.pdf

http://epub.ub.uni-muenchen.de/6175/2/tr036.pdf Minimize

Document Type:

text

Language:

en

Subjects:

R packages ; statistical computing ; software ; open source

R packages ; statistical computing ; software ; open source Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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