Package index • tsn

Package

Build distance, visibility, and state-transition networks from one or many univariate time series.

Turn continuous measurements into states, and classify the direction of change between consecutive observations.

Discretize a series and hand it to the matching Nestimate transition-network model, keeping the source data attached.

ts_tna() ts_ftna() ts_cna() ts_atna(): Transition Network Analysis of Time Series
series_networks(): Per-Series Transition Networks

Standard interface shared by every result object.

as.data.frame(<tsn>): Coerce a TSN result to a data frame
as.data.frame(<ts_tna>): Coerce a transition network to a tidy edge table
as.data.frame(<ts_tna_group>): Coerce a grouped transition network to a tidy data frame
as.matrix(<tsn>): Coerce a TSN result to a weighted adjacency matrix
as.matrix(<ts_tna>): Coerce a transition network to a weighted adjacency matrix
plot(<tsn>): Plot a TSN Result
plot(<tsn_series_networks>): Plot One Per-Series Transition Network
plot(<tsn_states>): Plot a state discretization
plot(<tsn_trend>): Plot a trend classification
plot(<ts_tna>): Plot a Time-Series Transition Network
print(<tsn>): Print a TSN network
print(<tsn_states>): Print a state discretization
print(<tsn_trend>): Print a trend classification
summary(<tsn>): Summarize a TSN network
summary(<tsn_series_networks>) as.data.frame(<tsn_series_networks>) print(<tsn_series_networks>): Summarize Per-Series Transition Networks
summary(<tsn_states>): Summarize a state discretization
summary(<tsn_trend>): Summarize a trend classification