depmix
depmix: Dependent Mixture Models. Fit (multigroup) mixtures of latent Markov models on mixed categorical and continuous (timeseries) data
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
Sorted by year (- Chow, Sy-Miin; Ou, Lu; Ciptadi, Arridhana; Prince, Emily B.; You, Dongjun; Hunter, Michael D.; Rehg, James M.; Rozga, Agata; Messinger, Daniel S.: Representing sudden shifts in intensive dyadic interaction data using differential equation models with regime switching (2018)
- Zuanetti, Daiane Aparecida; Milan, Luis Aparecido: A generalized mixture model applied to diabetes incidence data (2017)
- Kazor, Karen; Hering, Amanda S.: Assessing the performance of model-based clustering methods in multivariate time series with application to identifying regional wind regimes (2015)
- Visser, Ingmar; Speekenbrink, Maarten: Comments on: “Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates” (2014)
- Chow, Sy-Miin; Zhang, Guangjian: Nonlinear regime-switching state-space (RSSS) models (2013)
- Colombi, Roberto; Giordano, Sabrina: Testing lumpability for marginal discrete hidden Markov models (2011)
- Visser, Ingmar: Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series (2011)