Function reference • mixvlmc

Context trees

Build context trees and extract information from them.

ctx_tree(): Build a context tree for a discrete time series

context_number(): Number of contexts of a context tree

contexts(): Contexts of a context tree

contexts(<ctx_tree>) contexts(<ctx_tree_cpp>): Contexts of a context tree

depth(): Depth of a context tree

draw(<ctx_tree_cpp>) draw(<ctx_tree>): Text based representation of a context tree

draw_control(): Control parameters for draw

print(<contexts>): Print a context list

states(): State space of a context tree

Context tree nodes

Extract nodes from context trees and manipulate them.

find_sequence(): Find the node of a sequence in a context tree

find_sequence(<covlmc>): Find the node of a sequence in a COVLMC context tree

as_sequence(): Extract the sequence encoded by a node

children(): Find the children nodes of a node in a context tree

covariate_memory(): Covariate memory length for a COVLMC context

counts(): Report the distribution of values that follow occurrences of a sequence

cutoff(<ctx_node>): Cut off value for pruning a node in the context tree of a VLMC

is_context(): Report the nature of a node in a context tree

is_merged(): Merging status of a COVLMC context

is_reversed(): Report the ordering convention of the node

merged_with(): Merged contexts in a COVLMC

metrics(<ctx_node>): Predictive quality metrics for a node of a context tree

metrics(<ctx_node_covlmc>): Predictive quality metrics for a node of a COVLMC context tree

model(): Logistic model of a COVLMC context

parent(): Find the parent of a node in a context tree

positions(): Report the positions of a sequence associated to a node

rev(<ctx_node>): Reverse Sequence

Variable Length Markov Chain (VLMC)

Estimate VLMC from a time series and extract information from them.

vlmc(): Fit a Variable Length Markov Chain (VLMC)

context_number(): Number of contexts of a context tree

contexts(<vlmc>) contexts(<vlmc_cpp>): Contexts of a VLMC

depth(): Depth of a context tree

draw(<vlmc>) draw(<vlmc_cpp>): Text based representation of a vlmc

draw_control(): Control parameters for draw

logLik(<vlmc>) logLik(<vlmc_cpp>): Log-Likelihood of a VLMC

loglikelihood(): Log-Likelihood of a VLMC

metrics(<vlmc>) print(<metrics.vlmc>): Predictive quality metrics for VLMC

predict(<vlmc>) predict(<vlmc_cpp>): Next state prediction in a discrete time series for a VLMC

states(): State space of a context tree

Variable Length Markov Chain with covariates (COVLMC)

Estimate COVLMC from a time series and covariates. Extract information from them.

covlmc(): Fit a Variable Length Markov Chain with Covariates (coVLMC)

covlmc_control(): Control for coVLMC fitting

context_number(<covlmc>): Contexts number of a VLMC with covariates

contexts(<covlmc>): Contexts of a VLMC with covariates

covariate_depth(): Maximal covariate memory of a VLMC with covariates

depth(): Depth of a context tree

draw(<covlmc>): Text based representation of a covlmc model

draw_control(): Control parameters for draw

logLik(<covlmc>): Log-Likelihood of a VLMC with covariates

loglikelihood(<covlmc>): Log-Likelihood of a VLMC with covariates

metrics(<covlmc>) print(<metrics.covlmc>): Predictive quality metrics for VLMC with covariates

predict(<covlmc>): Next state prediction in a discrete time series for a VLMC with covariates

states(): State space of a context tree

Model selection for (CO)VLMC

Functions to adjust automatically or manually the complexity of a (CO)VLMC to the time series.

VLMC

tune_vlmc(): Fit an optimal Variable Length Markov Chain (VLMC)

plot(<tune_vlmc>) plot(<tune_covlmc>): Plot the results of automatic (CO)VLMC complexity selection

autoplot(<tune_vlmc>): Create a complete ggplot for the results of automatic VLMC complexity selection

cutoff(<vlmc>) cutoff(<vlmc_cpp>): Cut off values for pruning the context tree of a VLMC

prune(): Prune a Variable Length Markov Chain (VLMC)

COVLMC

tune_covlmc(): Fit an optimal Variable Length Markov Chain with Covariates (coVLMC)

plot(<tune_vlmc>) plot(<tune_covlmc>): Plot the results of automatic (CO)VLMC complexity selection

autoplot(<tune_covlmc>): Create a complete ggplot for the results of automatic COVLMC complexity selection

cutoff(<covlmc>): Cut off values for pruning the context tree of a VLMC with covariates

prune(<covlmc>): Prune a Variable Length Markov Chain with covariates

Simulation

Functions to simulate new time series from a (CO)VLMC model

simulate(<covlmc>): Simulate a discrete time series for a covlmc

simulate(<vlmc>): Simulate a discrete time series for a vlmc

simulate(<vlmc_cpp>): Simulate a discrete time series for a vlmc

Global options

mixvlmc mixvlmc-package: mixvlmc: Variable Length Markov Chains with Covariates

Data sets

globalearthquake: Significant Earthquake Dataset

powerconsumption: Individual household electric power consumption

Conversions and other low level manipulation of models

as_covlmc(): Convert an object to a Variable Length Markov Chain with covariates (coVLMC)

as_sequence(): Extract the sequence encoded by a node

as_vlmc(): Convert an object to a Variable Length Markov Chain (VLMC)

as_vlmc(<ctx_tree_cpp>): Convert an object to a Variable Length Markov Chain (VLMC)

is_context(): Report the nature of a node in a context tree

is_covlmc(): Test if the object is a covlmc model

is_ctx_tree(): Test if the object is a context tree

is_merged(): Merging status of a COVLMC context

is_reversed(): Report the ordering convention of the node

is_vlmc(): Test if the object is a vlmc model

trim(): Trim a context tree

trim(<covlmc>): Trim a COVLMC

trim(<vlmc>): This function returns a trimmed VLMC from which match positions have been removed.

trim(<vlmc_cpp>): This function returns a trimmed VLMC from which match positions have been removed.

Generics

Generic S3 methods defined by the package

cutoff(): Cut off values for VLMC like model

draw(): Text based representation of a context tree

metrics(): Predictive quality metrics for context based models