The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
Open accessAbstract
Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation.mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis.Recently, version 5 of the package has been made available on CRAN.This updated version 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.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
An Object-Oriented Framework for Robust Multivariate Analysis
Todorov, V., & Filzmoser, P. (2009) · Journal of Statistical Software · doi:10.18637/jss.v032.i03 · Cited by 420
Cited by
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
The Immune Landscape of Cancer
Thorsson, V., Gibbs, D. L., Brown, S. D., Wolf, D., Bortone, D. S., Ou Yang, T.-H., Porta-Pardo, E., Gao, G. F., Plaisier, C. L., Eddy, J. A., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.03.023 · Cited by 5,870
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Street, K., Risso, D., Fletcher, R. B., Das, D., Ngai, J., Yosef, N., Purdom, E., & Dudoit, S. (2018) · BMC Genomics · doi:10.1186/s12864-018-4772-0 · Cited by 3,159
Hoadley, K. A., Yau, C., Hinoue, T., Wolf, D. M., Lazar, A. J., Drill, E., Shen, R., Taylor, A. M., Cherniack, A. D., Thorsson, V., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.022 · Cited by 2,546
“Clustering of tumor samples scored on these five signatures was performed using model-based clustering, using the mclust R package (Scrucca et al., 2016), with the number of clusters, K, determined by maximization of Bayesian Information Criterion (BIC).”
MethodSingle-cell chromatin state analysis with Signac
Stuart, T., Srivastava, A., Madad, S., Lareau, C. A., & Satija, R. (2021) · Nature Methods · doi:10.1038/s41592-021-01282-5 · Cited by 1,769
Metadata
- Authors
- Scrucca, L., Fop, M., Murphy, T., & Raftery, A.
- Venue
- The R Journal
- Publisher
- The R Foundation
- Published
- 2016
- Volume
- 8
- Issue
- 1
- Pages
- 289
- ISSN
- 2073-4859 (ISSN-L)
- OpenAlex
- W2519132385
- Cited by
- 2,892
- References
- 3
- FWCI
- 117.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
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