The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R
Open accessAbstract
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.
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Cites
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Model-Based Clustering, Discriminant Analysis, and Density Estimation
Fraley, C., & Raftery, A. E. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502760047131 · Cited by 4,257
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Diebolt, J., & Robert, C. P. (1994) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1994.tb01985.x · Cited by 912
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A maximum likelihood methodology for clusterwise linear regression
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Self-citationA mixture likelihood approach for generalized linear models
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Cited by
Cribari-Neto, F., & Zeileis, A. (2010) · Journal of Statistical Software · doi:10.18637/jss.v034.i02 · Cited by 2,505
Regression Models for Count Data in R
Zeileis, A., Kleiber, C., & Jackman, S. (2008) · Journal of Statistical Software · doi:10.18637/jss.v027.i08 · Cited by 2,346
poLCA: An R Package for Polytomous Variable Latent Class Analysis
Linzer, D. A., & Lewis, J. B. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i10 · Cited by 1,636
Lacy, S. E., Barrans, S. L., Beer, P. A., Painter, D., Smith, A. G., Roman, E., Cooke, S. L., Ruiz, C., Glover, P., Van Hoppe, S. J. L., et al. (2020) · Blood · doi:10.1182/blood.2019003535 · Cited by 506
On-Line Expectation–Maximization Algorithm for latent Data Models
Cappé, O., & Moulines, E. (2009) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2009.00698.x · Cited by 477
Metadata
- Authors
- Leisch, F.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2004
- Volume
- 11
- Issue
- 8
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2159135906
- Cited by
- 677
- References
- 14
- FWCI
- 8.13
- Type
- journal article
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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