Package index • ggchangepoint

Core API

cpt_detect(): Unified changepoint detection dispatcher
cpt_methods(): Introspect available changepoint detection methods
cpt_penalty(): Construct changepoint penalties
cpt_cite(): Cite the method behind a result
as_cpt_series(): Coerce a time series object to values plus a time index
new_ggcpt(): Create a ggcpt object
is_ggcpt(): Test if an object is a ggcpt object
print(<ggcpt>): Print a ggcpt object

Extending the package

Bring a detector this package does not wrap – a non-CRAN engine, a Python tool, a neural detector, or your own changepoints – into the same tidy, plottable grammar.

as_ggcpt(): Turn external changepoints into a ggcpt result
cpt_register_method() cpt_unregister_method() cpt_registered_methods(): Register an external changepoint detector
cpt_install_engines(): Install the engines behind a family of methods

Original wrappers

cpt_wrapper(): Changepoint wrapper
ecp_wrapper(): ecp wrapper

Search and pruning wrappers

fpop_wrapper(): FPOP wrapper — Functional Pruning Optimal Partitioning
wbs_wrapper(): WBS wrapper — Wild Binary Segmentation
wbs2_wrapper(): WBS2 wrapper — Wild Binary Segmentation 2
not_wrapper(): NOT wrapper — Narrowest-Over-Threshold
mosum_wrapper(): MOSUM wrapper — Moving Sum
idetect_wrapper(): Isolate-Detect wrapper
tguh_wrapper(): TGUH wrapper

Inference and slope wrappers

smuce_wrapper(): SMUCE / HSMUCE wrapper — multiscale changepoint inference
cpop_wrapper(): CPOP wrapper — optimal change-in-slope detection
nsp_wrapper(): NSP wrapper — Narrowest Significance Pursuit
cpt_crops() autoplot(<ggcpt_path>) print(<ggcpt_path>) tidy(<ggcpt_path>): CROPS — the full penalty path of a penalised changepoint method

Inference

Where could this changepoint be, and is it real?

cpt_confint(): Confidence intervals for changepoint locations
cpt_test(): Test detected changepoints
cpt_regions(): Tidy the significance regions of a ggcpt object

Choosing the number of changepoints

cpt_select() print(<ggcpt_selection>) tidy(<ggcpt_selection>) autoplot(<ggcpt_selection>): Choose the number of changepoints

Diagnostics

Which observation is driving this, which setting, and what did the detector actually compute?

cpt_influence() print(<ggcpt_influence>) autoplot(<ggcpt_influence>) tidy(<ggcpt_influence>): Influence diagnostics for a changepoint segmentation
cpt_leverage(): Rank observations by influence
cpt_sensitivity() print(<ggcpt_sensitivity>) tidy(<ggcpt_sensitivity>) autoplot(<ggcpt_sensitivity>): Sensitivity of a segmentation to its tuning parameters
cpt_statistic() ggcpt_statistic(): The detector's statistic as a function of location
cpt_solution_path() ggcpt_solution_path(): The solution path of a search-based detector
cpt_scale_space() ggcpt_scale_space(): Scale space: the statistic across bandwidths

Supervised detection

Labelled regions as ground truth, label errors as the accuracy measure, and a learned penalty.

cpt_labels(): Changepoint labels
as_cpt_labels(): Coerce annotations to changepoint labels
cpt_label_error() tidy(<cpt_label_error>) print(<cpt_label_error>): Score a segmentation against labels
cpt_label_error_curve() print(<ggcpt_label_curve>) autoplot(<ggcpt_label_curve>): Label error as a function of the penalty
cpt_learn_penalty() print(<ggcpt_penalty_model>) coef(<ggcpt_penalty_model>) predict(<ggcpt_penalty_model>): Learn a penalty from labelled series

Choosing and combining methods

cpt_recommend() tidy(<ggcpt_recommendation>) print(<ggcpt_recommendation>): Recommend a detection method
cpt_consensus() autoplot(<ggcpt_consensus>) print(<ggcpt_consensus>): Consensus changepoints across several detectors

Communication

cpt_annotate_events() print(<ggcpt_events>) tidy(<ggcpt_events>) autoplot(<ggcpt_events>): Match detected changepoints to known events
cpt_report(): A reproducible report of a changepoint analysis
cpt_gt(): A publication-ready changepoint table

Benchmarking

cpt_benchmark() print(<ggcpt_benchmark>) tidy(<ggcpt_benchmark>) autoplot(<ggcpt_benchmark>): Benchmark detectors across datasets
cpt_datasets(): A catalogue of benchmark datasets
cpt_load_tcpd(): Download and cache the Turing Change Point Dataset
cpt_annotations(): Per-annotator ground truth for a benchmark dataset

Streaming and online monitoring

cpt_monitor() tidy(<ggcpt_monitor>) print(<ggcpt_monitor>) autoplot(<ggcpt_monitor>): A stateful sequential changepoint monitor
cpt_update(): Feed observations to a monitor
alarms(): The alarm log of a monitor
cpt_replay(): Replay a series through a sequential detector
cpt_delay() tidy(<ggcpt_delay>) glance(<ggcpt_delay>) print(<ggcpt_delay>) autoplot(<ggcpt_delay>): Detection delay and false-alarm rate

Power and study design

cpt_power() tidy(<ggcpt_power>) print(<ggcpt_power>) autoplot(<ggcpt_power>): Detection power for a changepoint scenario
cpt_min_detectable() print(<ggcpt_min_detectable>): The smallest detectable change
cpt_scenarios(): A grid of simulation scenarios

Bayesian wrappers

bcp_wrapper(): Bayesian changepoint wrapper (Barry-Hartigan product partition model)
bocpd_wrapper(): Bayesian online changepoint detection wrapper (BOCPD)
beast_wrapper(): BEAST wrapper — Bayesian estimation of abrupt change, seasonality, and trend

Nonparametric and sequential wrappers

cpm_wrapper(): Sequential change point model wrapper (CPM)
kcp_wrapper(): Kernel changepoint wrapper (KCP on running statistics)
npmojo_wrapper(): Nonparametric MOSUM wrapper (NP-MOJO)
sn_wrapper(): Self-normalisation wrapper (SNSeg)

Robust and model-selection wrappers

decafs_wrapper(): DeCAFS wrapper — changes amid drift and autocorrelated noise
envcpt_wrapper(): EnvCpt wrapper — changepoints versus trends versus autocorrelation
fastcpd_wrapper(): fastcpd wrapper — fast changepoint detection via sequential gradient descent

Multivariate and high-dimensional wrappers

inspect_wrapper(): inspect wrapper — high-dimensional changepoints via sparse projection
ocd_wrapper(): ocd wrapper — online high-dimensional changepoint detection
geomcp_wrapper(): Geometrically-inspired multivariate changepoint wrapper (geomcp)
esac_wrapper(): ESAC wrapper — sparsity-adaptive high-dimensional detection
pilliat_wrapper(): Pilliat wrapper — high-dimensional detection by three complementary tests
hdcov_wrapper(): High-dimensional covariance changepoints
network_wrapper(): Dynamic-network changepoints
var_wrapper(): VAR(1) changepoints
hdreg_wrapper(): High-dimensional regression changepoints

Functional and network wrappers

fmean_wrapper(): Functional mean changepoints
fcov_wrapper(): Functional covariance changepoints
kwc_wrapper(): Robust depth-based changepoints for functional and multivariate data
fabisearch_wrapper(): Network-structure changepoints via non-negative matrix factorisation

Regression-break wrappers

strucchange_wrapper(): Bai-Perron structural break wrapper (strucchange)
segmented_wrapper(): Broken-line regression wrapper (segmented)
bfast_wrapper(): BFAST wrapper — breaks for additive season and trend

Applied vocabularies and fast paths

trend_wrapper(): Classical single-changepoint tests (Pettitt, Buishand, SNHT)
taylor_wrapper(): Taylor's change point analyzer
wbsts_wrapper(): WBS for nonstationary time series
binsegrcpp_wrapper(): Fast binary segmentation across loss functions

Bayesian formula models

mcp_wrapper(): Bayesian formula-based changepoint regression (mcp)

broom methods

tidy(<ggcpt>): Tidy a ggcpt object
glance(<ggcpt>): Glance at a ggcpt object
augment(<ggcpt>): Augment a ggcpt object
summary(<ggcpt>) print(<summary.ggcpt>): Summary of a ggcpt object

Additional S3 methods

as_tibble(<ggcpt>) as.data.frame(<ggcpt>) format(<ggcpt>) plot(<ggcpt>): Coerce, format, and plot ggcpt objects
plot(<ggcpt_selection>) plot(<ggcpt_stability>) plot(<ggcpt_sensitivity>) plot(<ggcpt_influence>) plot(<ggcpt_batch>) plot(<ggcpt_benchmark>) plot(<ggcpt_consensus>) plot(<ggcpt_monitor>) plot(<ggcpt_delay>) plot(<ggcpt_path>) plot(<ggcpt_power>) plot(<ggcpt_events>) plot(<ggcpt_label_curve>): Base plot() methods for ggchangepoint result objects

Theming, palettes and accessibility

theme_ggcpt(): ggchangepoint theme
annotate_segments(): Annotate segments with alternating shading
scale_colour_cpt() scale_color_cpt() scale_fill_cpt() scale_linetype_cpt(): Colour-vision-safe scales for changepoint methods
scale_fill_cpt_label() scale_colour_cpt_label(): Colour scales for changepoint labels and label errors

Visualization

autoplot(<ggcpt>): Autoplot a ggcpt object
ggcptplot(): Plot for the changepoint package
ggecpplot(): Plot for the ecp package
geom_changepoint(): Changepoint vertical rules geom
geom_cpt_segment(): Changepoint segment level geom
geom_cpt_ci(): Changepoint confidence interval geom
geom_cpt_region(): Significance region geom
geom_cpt_label(): Changepoint label geom
geom_cpt_event(): Event annotation geom
stat_changepoint(): Changepoint detection stat
ggcpt_posterior(): Posterior probability plot for Bayesian results
ggcpt_runlength(): Run-length posterior heatmap for Bayesian online results
ggcpt_interactive(): Interactive changepoint plot

Method comparison, batch, and stability

ggcpt_compare(): Compare multiple changepoint detection methods
ggcpt_compare_table(): Comparison table
cpt_batch() print(<ggcpt_batch>) tidy(<ggcpt_batch>) autoplot(<ggcpt_batch>): Batch changepoint detection over many series
cpt_stability() print(<ggcpt_stability>) autoplot(<ggcpt_stability>): Changepoint stability diagnostics via bootstrap

Evaluation

cpt_metrics(): Changepoint accuracy metrics
cpt_metrics_annotated(): Multi-annotator evaluation
ggcpt_eval(): Evaluation visualization

Simulation and data

cpt_simulate() rcpt(): Generate simulated changepoint data
signal_blocks(): Blocks test signal
signal_fms(): FMS (Four-Metric-Segments) test signal
signal_mix(): Mix test signal
signal_teeth(): Teeth test signal
signal_stairs(): Stairs test signal

Re-exports

reexports tidy glance augment autoplot as_tibble: Objects exported from other packages