Combines Changepoint Analysis with 'ggplot2'

Documentation for package ‘ggchangepoint’ version 0.5.0

Help Pages

-- A --

alarms	The alarm log of a monitor
alarms.ggcpt_monitor	The alarm log of a monitor
annotate_segments	Annotate segments with alternating shading
as.data.frame.ggcpt	Coerce, format, and plot ggcpt objects
as_cpt_labels	Coerce annotations to changepoint labels
as_cpt_series	Coerce a time series object to values plus a time index
as_ggcpt	Turn external changepoints into a ggcpt result
as_tibble.ggcpt	Coerce, format, and plot ggcpt objects
augment.ggcpt	Augment a ggcpt object
autoplot.ggcpt	Autoplot a ggcpt object
autoplot.ggcpt_batch	Batch changepoint detection over many series
autoplot.ggcpt_benchmark	Benchmark detectors across datasets
autoplot.ggcpt_consensus	Consensus changepoints across several detectors
autoplot.ggcpt_delay	Detection delay and false-alarm rate
autoplot.ggcpt_events	Match detected changepoints to known events
autoplot.ggcpt_influence	Influence diagnostics for a changepoint segmentation
autoplot.ggcpt_label_curve	Label error as a function of the penalty
autoplot.ggcpt_monitor	A stateful sequential changepoint monitor
autoplot.ggcpt_path	CROPS: the full penalty path of a penalised changepoint method
autoplot.ggcpt_power	Detection power for a changepoint scenario
autoplot.ggcpt_selection	Choose the number of changepoints
autoplot.ggcpt_sensitivity	Sensitivity of a segmentation to its tuning parameters
autoplot.ggcpt_stability	Changepoint stability diagnostics via bootstrap

-- B --

bcp_wrapper	Bayesian changepoint wrapper (Barry-Hartigan product partition model)
beast_wrapper	BEAST wrapper: Bayesian estimation of abrupt change, seasonality, and trend
bfast_wrapper	BFAST wrapper: breaks for additive season and trend
binsegrcpp_wrapper	Fast binary segmentation across loss functions
bocpd_wrapper	Bayesian online changepoint detection wrapper (BOCPD)

-- C --

coef.ggcpt_penalty_model	Learn a penalty from labelled series
cpm_wrapper	Sequential change point model wrapper (CPM)
cpop_wrapper	CPOP wrapper: optimal change-in-slope detection
cpt_annotate_events	Match detected changepoints to known events
cpt_annotations	Per-annotator ground truth for a benchmark dataset
cpt_batch	Batch changepoint detection over many series
cpt_benchmark	Benchmark detectors across datasets
cpt_cite	Cite the method behind a result
cpt_confint	Confidence intervals for changepoint locations
cpt_consensus	Consensus changepoints across several detectors
cpt_crops	CROPS: the full penalty path of a penalised changepoint method
cpt_datasets	A catalogue of benchmark datasets
cpt_delay	Detection delay and false-alarm rate
cpt_detect	Unified changepoint detection dispatcher
cpt_gt	A publication-ready changepoint table
cpt_influence	Influence diagnostics for a changepoint segmentation
cpt_install_engines	Install the engines behind a family of methods
cpt_labels	Changepoint labels
cpt_label_error	Score a segmentation against labels
cpt_label_error_curve	Label error as a function of the penalty
cpt_learn_penalty	Learn a penalty from labelled series
cpt_leverage	Rank observations by influence
cpt_load_tcpd	Download and cache the Turing Change Point Dataset
cpt_methods	Introspect available changepoint detection methods
cpt_metrics	Changepoint accuracy metrics
cpt_metrics_annotated	Multi-annotator evaluation
cpt_min_detectable	The smallest detectable change
cpt_monitor	A stateful sequential changepoint monitor
cpt_penalty	Construct changepoint penalties
cpt_power	Detection power for a changepoint scenario
cpt_recommend	Recommend a detection method
cpt_regions	Tidy the significance regions of a ggcpt object
cpt_registered_methods	Register an external changepoint detector
cpt_register_method	Register an external changepoint detector
cpt_replay	Replay a series through a sequential detector
cpt_report	A reproducible report of a changepoint analysis
cpt_scale_space	Scale space: the statistic across bandwidths
cpt_scenarios	A grid of simulation scenarios
cpt_select	Choose the number of changepoints
cpt_sensitivity	Sensitivity of a segmentation to its tuning parameters
cpt_simulate	Generate simulated changepoint data
cpt_solution_path	The solution path of a search-based detector
cpt_stability	Changepoint stability diagnostics via bootstrap
cpt_statistic	The detector's statistic as a function of location
cpt_test	Test detected changepoints
cpt_unregister_method	Register an external changepoint detector
cpt_update	Feed observations to a monitor
cpt_wrapper	Changepoint wrapper

-- D --

decafs_wrapper

DeCAFS wrapper: changes amid drift and autocorrelated noise

-- E --

ecp_wrapper	ecp wrapper
envcpt_wrapper	EnvCpt wrapper: changepoints versus trends versus autocorrelation
esac_wrapper	ESAC wrapper: sparsity-adaptive high-dimensional detection

-- F --

fabisearch_wrapper	Network-structure changepoints via non-negative matrix factorisation
fastcpd_wrapper	fastcpd wrapper: fast changepoint detection via sequential gradient descent
fcov_wrapper	Functional covariance changepoints
fmean_wrapper	Functional mean changepoints
format.ggcpt	Coerce, format, and plot ggcpt objects
fpop_wrapper	FPOP wrapper: Functional Pruning Optimal Partitioning

-- G --

geomcp_wrapper	Geometrically-inspired multivariate changepoint wrapper (geomcp)
geom_changepoint	Changepoint vertical rules geom
geom_cpt_ci	Changepoint confidence interval geom
geom_cpt_event	Event annotation geom
geom_cpt_label	Changepoint label geom
geom_cpt_region	Significance region geom
geom_cpt_segment	Changepoint segment level geom
ggcptplot	Plot for the changepoint package
ggcpt_compare	Compare multiple changepoint detection methods
ggcpt_compare_table	Comparison table
ggcpt_eval	Evaluation visualization
ggcpt_interactive	Interactive changepoint plot
ggcpt_methods	Coerce, format, and plot ggcpt objects
ggcpt_plot_methods	Base plot() methods for ggchangepoint result objects
ggcpt_posterior	Posterior probability plot for Bayesian results
ggcpt_runlength	Run-length posterior heatmap for Bayesian online results
ggcpt_scale_space	Scale space: the statistic across bandwidths
ggcpt_solution_path	The solution path of a search-based detector
ggcpt_statistic	The detector's statistic as a function of location
ggecpplot	Plot for the ecp package
glance.ggcpt	Glance at a ggcpt object
glance.ggcpt_delay	Detection delay and false-alarm rate

-- H --

hdcov_wrapper	High-dimensional covariance changepoints
hdreg_wrapper	High-dimensional regression changepoints

-- I --

idetect_wrapper	Isolate-Detect wrapper
inspect_wrapper	inspect wrapper: high-dimensional changepoints via sparse projection
is_ggcpt	Test if an object is a ggcpt object

-- K --

kcp_wrapper	Kernel changepoint wrapper (KCP on running statistics)
kwc_wrapper	Robust depth-based changepoints for functional and multivariate data

-- M --

mcp_wrapper	Bayesian formula-based changepoint regression (mcp)
mosum_wrapper	MOSUM wrapper: Moving Sum

-- N --

network_wrapper	Dynamic-network changepoints
new_ggcpt	Create a ggcpt object
not_wrapper	NOT wrapper: Narrowest-Over-Threshold
npmojo_wrapper	Nonparametric MOSUM wrapper (NP-MOJO)
nsp_wrapper	NSP wrapper: Narrowest Significance Pursuit

-- O --

ocd_wrapper

ocd wrapper: online high-dimensional changepoint detection

-- P --

pilliat_wrapper	Pilliat wrapper: high-dimensional detection by three complementary tests
plot.ggcpt	Coerce, format, and plot ggcpt objects
plot.ggcpt_batch	Base plot() methods for ggchangepoint result objects
plot.ggcpt_benchmark	Base plot() methods for ggchangepoint result objects
plot.ggcpt_consensus	Base plot() methods for ggchangepoint result objects
plot.ggcpt_delay	Base plot() methods for ggchangepoint result objects
plot.ggcpt_events	Base plot() methods for ggchangepoint result objects
plot.ggcpt_influence	Base plot() methods for ggchangepoint result objects
plot.ggcpt_label_curve	Base plot() methods for ggchangepoint result objects
plot.ggcpt_monitor	Base plot() methods for ggchangepoint result objects
plot.ggcpt_path	Base plot() methods for ggchangepoint result objects
plot.ggcpt_power	Base plot() methods for ggchangepoint result objects
plot.ggcpt_selection	Base plot() methods for ggchangepoint result objects
plot.ggcpt_sensitivity	Base plot() methods for ggchangepoint result objects
plot.ggcpt_stability	Base plot() methods for ggchangepoint result objects
predict.ggcpt_penalty_model	Learn a penalty from labelled series
print.cpt_label_error	Score a segmentation against labels
print.ggcpt	Print a ggcpt object
print.ggcpt_batch	Batch changepoint detection over many series
print.ggcpt_benchmark	Benchmark detectors across datasets
print.ggcpt_consensus	Consensus changepoints across several detectors
print.ggcpt_delay	Detection delay and false-alarm rate
print.ggcpt_events	Match detected changepoints to known events
print.ggcpt_influence	Influence diagnostics for a changepoint segmentation
print.ggcpt_label_curve	Label error as a function of the penalty
print.ggcpt_min_detectable	The smallest detectable change
print.ggcpt_monitor	A stateful sequential changepoint monitor
print.ggcpt_path	CROPS: the full penalty path of a penalised changepoint method
print.ggcpt_penalty_model	Learn a penalty from labelled series
print.ggcpt_power	Detection power for a changepoint scenario
print.ggcpt_recommendation	Recommend a detection method
print.ggcpt_selection	Choose the number of changepoints
print.ggcpt_sensitivity	Sensitivity of a segmentation to its tuning parameters
print.ggcpt_stability	Changepoint stability diagnostics via bootstrap
print.summary.ggcpt	Summary of a ggcpt object

-- R --

rcpt	Generate simulated changepoint data

-- S --

scale_color_cpt	Colour-vision-safe scales for changepoint methods
scale_colour_cpt	Colour-vision-safe scales for changepoint methods
scale_colour_cpt_label	Colour scales for changepoint labels and label errors
scale_fill_cpt	Colour-vision-safe scales for changepoint methods
scale_fill_cpt_label	Colour scales for changepoint labels and label errors
scale_linetype_cpt	Colour-vision-safe scales for changepoint methods
segmented_wrapper	Broken-line regression wrapper (segmented)
signal_blocks	Blocks test signal
signal_fms	FMS (Four-Metric-Segments) test signal
signal_mix	Mix test signal
signal_stairs	Stairs test signal
signal_teeth	Teeth test signal
smuce_wrapper	SMUCE / HSMUCE wrapper: multiscale changepoint inference
sn_wrapper	Self-normalisation wrapper (SNSeg)
stat_changepoint	Changepoint detection stat
strucchange_wrapper	Bai-Perron structural break wrapper (strucchange)
summary.ggcpt	Summary of a ggcpt object

-- T --

taylor_wrapper	Taylor's change point analyzer
tguh_wrapper	TGUH wrapper
theme_ggcpt	ggchangepoint theme
tidy.cpt_labels	Changepoint labels
tidy.cpt_label_error	Score a segmentation against labels
tidy.ggcpt	Tidy a ggcpt object
tidy.ggcpt_batch	Batch changepoint detection over many series
tidy.ggcpt_benchmark	Benchmark detectors across datasets
tidy.ggcpt_delay	Detection delay and false-alarm rate
tidy.ggcpt_events	Match detected changepoints to known events
tidy.ggcpt_influence	Influence diagnostics for a changepoint segmentation
tidy.ggcpt_monitor	A stateful sequential changepoint monitor
tidy.ggcpt_path	CROPS: the full penalty path of a penalised changepoint method
tidy.ggcpt_power	Detection power for a changepoint scenario
tidy.ggcpt_recommendation	Recommend a detection method
tidy.ggcpt_selection	Choose the number of changepoints
tidy.ggcpt_sensitivity	Sensitivity of a segmentation to its tuning parameters
trend_wrapper	Classical single-changepoint tests (Pettitt, Buishand, SNHT)

-- V --

var_wrapper

VAR(1) changepoints

-- W --

wbs2_wrapper	WBS2 wrapper: Wild Binary Segmentation 2
wbsts_wrapper	WBS for nonstationary time series
wbs_wrapper	WBS wrapper: Wild Binary Segmentation