Package index • rEMM

Package Overview

rEMM rEMM-package: Extensible Markov Models for Sequence Data

Models

EMM(): Class "EMM"
tNN(): Class "tNN"
TRACDS() as.igraph() as.graph(): Class "TRACDS"

Building models

build(): Building an EMM using New Data
cluster(): Data stream clustering with tNN
compact(<TRACDS>) reset(<TRACDS>) update(<TRACDS>): Update a TRACDS temporal structure with new state assignements

Working with the models

plot(<EMM>,<missing>): Visualize EMM Objects
find_clusters(<tNN>,<matrix>): Find the EMM State/Cluster for an Observation
recluster_hclust(<EMM>) recluster_kmeans(<EMM>) recluster_pam(<EMM>) recluster_reachability(<EMM>) recluster_tNN(<EMM>) recluster_transitions(<EMM>): Reclustering EMM states
merge_clusters(<EMM>,<character>): Merge States of an EMM
remove_clusters() remove_transitions() remove_selftransitions(): Remove States/Clusters or Transitions from an EMM
rare_transitions() rare_clusters() prune(<EMM>): Prune States and/or Transitions
fade(): Fading Cluster Structure and EMM Layer
c(<EMM>): Combining EMM Objects

Transitions and sequence analysis

transition(<TRACDS>,<character>,<character>) transition_matrix(<TRACDS>) initial_transition(<TRACDS>): Access Transition Probabilities/Counts in an EMM
transition_table(<EMM>,<matrix>): Extract a Transition Table for a New Sequence Given an EMM
smooth_transitions(<EMM>): Smooths transition counts between neighboring states/clusters
score(<EMM>,<matrix>) score(<EMM>,<EMM>): Score a New Sequence Given an EMM
predict(<TRACDS>): Predict a Future State

Temporal Structure for Standard Clustering Algorithms

TRAC(): TRAC: Creating a Markov Model from a Regular Clustering

Stream Interface Classes

DSC_EMM() get_EMM() set_EMM() DSC_tNN(): DSC Interface for EMM and tNN (package stream)

Data

synthetic_stream(): Create a Synthetic Data Stream
EMMsim: Synthetic Data to Demonstrate EMMs
EMMTraffic: Hypothetical Traffic Data Set for EMM
Derwent: Derwent Catchment Data
`16S`: Count Data for 16S rRNA Sequences