Introduction | Extensible Markov model | Threshold Nearest Neighbor clustering algorithm | Implementation details | Examples | Applications | Conclusion
Package: rEMM 1.2.3
rEMM: Extensible Markov Model for Modelling Temporal Relationships Between Clusters
Implements TRACDS (Temporal Relationships between Clusters for Data Streams), a generalization of the Extensible Markov Model (EMM). TRACDS adds a temporal or order model to data stream clustering by superimposing a dynamically adapting Markov Chain. It also provides an implementation of EMM (TRACDS on top of tNN data stream clustering). Development of this package was supported in part by NSF IIS-0948893 and R21HG005912 from the National Human Genome Research Institute. Hahsler and Dunham (2010) <doi:10.18637/jss.v035.i05>.
Authors:
rEMM_1.2.3.tar.gz
rEMM_1.2.3.zip(r-4.7-any)rEMM_1.2.3.zip(r-4.6-any)rEMM_1.2.3.zip(r-4.5-any)
rEMM_1.2.3.tgz(r-4.6-any)rEMM_1.2.3.tgz(r-4.5-any)
rEMM_1.2.3.tar.gz(r-4.7-any)rEMM_1.2.3.tar.gz(r-4.6-any)
rEMM_1.2.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rEMM/json (API)
| # Install 'rEMM' in R: |
| install.packages('rEMM', repos = c('https://mhahsler.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mhahsler/remm/issues
Pkgdown/docs site:https://michael.hahsler.net
Datasets:
- Alphaproteobacteria16S - Count Data for 16S rRNA Sequences
- Derwent - Derwent Catchment Data
- EMMsim_sequence_test - Synthetic Data to Demonstrate EMMs
- EMMsim_sequence_train - Synthetic Data to Demonstrate EMMs
- EMMsim_test - Synthetic Data to Demonstrate EMMs
- EMMsim_train - Synthetic Data to Demonstrate EMMs
- EMMTraffic - Hypothetical Traffic Data Set for EMM
- Mollicutes16S - Count Data for 16S rRNA Sequences
clusteringdata-streamsequence-analysis
Last updated from:4d5f22e220. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 183 | ||
| source / vignettes | OK | 259 | ||
| linux-release | OK | 157 | ||
| macos-release | OK | 103 | ||
| macos-oldrel | OK | 135 | ||
| windows-devel | OK | 85 | ||
| windows-release | OK | 90 | ||
| windows-oldrel | OK | 122 | ||
| wasm-release | OK | 132 |
Exports:as.graphas.graph.TRACDSas.igraphas.igraph.TRACDSbuildclustercluster_centerscluster_countsclusterscompactcopycurrent_stateDSC_EMMDSC_tNNEMMfadefind_clustersget_EMMinitial_transitionlast_clusteringmerge_clustersnclustersnstatesntransitionsobject.sizeplotpredictprunerare_clustersrare_transitionsrecluster_hclustrecluster_kmeansrecluster_pamrecluster_reachabilityrecluster_tNNrecluster_transitionsremove_clustersremove_selftransitionsremove_transitionsresetscoreset_EMMsizesmooth_transitionsstatessynthetic_streamtNNTRACTRACDStransitiontransition_matrixtransition_tabletransitionsupdate
Dependencies:BHclasscliclueclusterclusterGenerationcpp11dbscanDEoptimRdiptestflexmixfpcgenericsglueigraphkernlablatticelifecyclemagrittrMASSMatrixmclustmlbenchmodeltoolsnnetpkgconfigprabclusproxyRcpprlangrobustbaserpartstreamvctrs
Extensible Markov Model for data stream clustering
Last update: 2026-10-17
Started: 2026-10-08
Getting started with rEMM
Introduction | Install and load the package | Prepare an ordered sequence | Build and inspect an EMM | Simplifying the model | Score a new sequence | Predict a future state | Add observations incrementally | Next steps | References
Last update: 2026-10-17
Started: 2026-10-08
Adding temporal structure modeling to standard clustering
Introduction | Cluster the observations | Add the temporal model
Last update: 2026-10-08
Started: 2026-10-08
Adding temporal structure modeling to stream clustering
Introduction | Prepare the stream and models | Update both models | Inspect the temporal model | Predict the next state | Start an independent sequence | Use other stream clusterers | References
Last update: 2026-10-08
Started: 2026-10-08
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Count Data for 16S rRNA Sequences | 16S Alphaproteobacteria16S Mollicutes16S rRNA16S |
| Building an EMM using New Data | build build,EMM,data.frame-method build,EMM,matrix-method build,EMM,numeric-method |
| Data stream clustering with tNN | cluster cluster,tNN,data.frame-method cluster,tNN,matrix-method cluster,tNN,numeric-method |
| Combining EMM Objects | c c,EMM-method combine |
| Derwent Catchment Data | Derwent |
| DSC Interface for EMM and tNN (package stream) | DSC_EMM DSC_tNN get_EMM set_EMM |
| Class "EMM" | copy copy,EMM-method EMM EMM-class object.size,EMM-method show,EMM-method size size,EMM-method |
| Synthetic Data to Demonstrate EMMs | EMMsim EMMsim_sequence_test EMMsim_sequence_train EMMsim_test EMMsim_train |
| Hypothetical Traffic Data Set for EMM | EMMTraffic |
| Fading Cluster Structure and EMM Layer | fade fade,EMM,missing,missing-method fade,EMM,missing,numeric-method fade,EMM,numeric,missing-method fade,EMM,numeric,numeric-method |
| Find the EMM State/Cluster for an Observation | find_clusters find_clusters,tNN,data.frame-method find_clusters,tNN,matrix-method find_clusters,tNN,numeric-method |
| Merge States of an EMM | merge_clusters merge_clusters,EMM,character-method merge_clusters,EMM,integer-method |
| Visualize EMM Objects | plot plot,EMM,missing-method |
| Predict a Future State | predict predict,TRACDS-method |
| Prune States and/or Transitions | prune prune,EMM-method rare_clusters rare_clusters,tNN-method rare_transitions rare_transitions,TRACDS-method |
| Reclustering EMM states | recluster recluster_hclust recluster_hclust,EMM-method recluster_kmeans recluster_kmeans,EMM-method recluster_pam recluster_pam,EMM-method recluster_reachability recluster_reachability,EMM-method recluster_tNN recluster_tNN,EMM-method recluster_transitions recluster_transitions,EMM-method |
| Remove States/Clusters or Transitions from an EMM | remove remove_clusters remove_clusters,EMM,character-method remove_selftransitions remove_selftransitions,EMM-method remove_transitions remove_transitions,EMM,character,character-method remove_transitions,EMM,matrix,missing-method |
| Score a New Sequence Given an EMM | score score,EMM,data.frame-method score,EMM,EMM-method score,EMM,matrix-method score,EMM,numeric-method |
| Smooths transition counts between neighboring states/clusters | smooth_transitions smooth_transitions,EMM-method |
| Create a Synthetic Data Stream | synthetic_stream |
| Class "tNN" | clusters clusters,tNN-method cluster_centers cluster_centers,tNN-method cluster_counts cluster_counts,tNN-method copy,tNN-method last_clustering last_clustering,tNN-method nclusters nclusters,tNN-method object.size,tNN-method plot,tNN,missing-method StreamClustering-class tNN tNN-class |
| TRAC: Creating a Markov Model from a Regular Clustering | TRAC |
| Class "TRACDS" | as.graph as.graph.TRACDS as.igraph as.igraph.TRACDS copy,TRACDS-method current_state current_state,TRACDS-method nstates nstates,TRACDS-method ntransitions ntransitions,TRACDS-method object.size,TRACDS-method plot,TRACDS,missing-method show,TRACDS-method states states,TRACDS-method TRACDS TRACDS-class transitions transitions,TRACDS-method |
| Access Transition Probabilities/Counts in an EMM | initial_transition initial_transition,TRACDS-method transition transition,TRACDS,character,character-method transition,TRACDS,data.frame,missing-method transition,TRACDS,matrix,missing-method transition_matrix transition_matrix,TRACDS-method |
| Extract a Transition Table for a New Sequence Given an EMM | transition_table transition_table,EMM,data.frame-method transition_table,EMM,matrix-method transition_table,EMM,numeric-method |
| Update a TRACDS temporal structure with new state assignements | compact compact,TRACDS-method reset reset,TRACDS-method update update,TRACDS-method |