hmmm: An R Package for Hierarchical Multinomial Marginal Models
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Abstract
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DOI: http://hdl.handle.net/10.18637/jss.v059.i11
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References listed on IDEAS
- Marchetti, Giovanni M., 2006. "Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i06).
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Cited by:
- R. Colombi & A. Forcina, 2016. "Testing order restrictions in contingency tables," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(1), pages 73-90, January.
- Roberto Colombi & Sabrina Giordano, 2019. "Likelihood-based tests for a class of misspecified finite mixture models for ordinal categorical data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1175-1202, December.
- Colombi, Roberto, 2020. "Selection tests for possibly misspecified hierarchical multinomial marginal models," Econometrics and Statistics, Elsevier, vol. 16(C), pages 136-147.
- Colombi, R. & Giordano, S., 2015. "Multiple hidden Markov models for categorical time series," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 19-30.
- Federica Nicolussi & Manuela Cazzaro, 2020. "Context-specific independencies in hierarchical multinomial marginal models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 767-786, December.
- Alan Agresti & Sabrina Giordano & Anna Gottard, 2022. "A Review of Score-Test-Based Inference for Categorical Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 31-48, September.
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