Package index • tidychangepoint

All functions

BMDL(): Bayesian Maximum Descriptive Length
CET: Hadley Centre Central England Temperature
DataCPSim rlnorm_ts_1 rlnorm_ts_2 rlnorm_ts_3: Simulated time series data
HQC(): Hannan–Quinn information criterion
MBIC(): Modified Bayesian Information Criterion
MDL(): Maximum Descriptive Length
SIC(): Schwarz information criterion
as.model() is_model(): Convert, retrieve, or verify a model object
as.segmenter() as.seg_cpt() is_segmenter(): Convert, retrieve, or verify a segmenter object
as_year(): Convert a date into a year
binary2tau() tau2binary(): Convert changepoint sets to binary strings
bogota_pm: Particulate matter in Bogotá, Colombia
build_gabin_population() log_gabin_population(): Initialize populations in genetic algorithms
changepoints(): Extract changepoints
compare_models() compare_algorithms(): Compare various models or algorithms for a given changepoint set
cut_by_tau() split_by_tau(): Use a changepoint set to break a time series into regions
deg_free(): Retrieve the degrees of freedom from a logLik object
diagnose(): Diagnose the fit of a segmented time series
exceedances(): Compute exceedances of a threshold for a time series
file_name(): Obtain a descriptive filename for a tidycpt object
fit_arima(): Fit an ARIMA model
fit_lmshift() fit_lmshift_ar1() fit_trendshift() fit_trendshift_ar1(): Regression-based model fitting
fit_meanshift() fit_meanshift_norm() fit_meanshift_lnorm() fit_meanshift_norm_ar1(): Fast implementation of meanshift model
fit_meanvar(): Fit a model for mean and variance
fit_nhpp(): Fit a non-homogeneous Poisson process model to the exceedances of a time series.
fitness(): Retrieve the optimal fitness (or objective function) value used by an algorithm
new_fun_cpt() validate_fun_cpt() fun_cpt(): Class for model-fitting functions
italy_grads: Italian University graduates by disciplinary groups from 1926-2013
iweibull() mweibull() parameters_weibull(): Weibull distribution functions
ls_models() ls_pkgs() ls_methods() ls_penalties() ls_cpt_penalties() ls_coverage(): Algorithmic coverage through tidychangepoint
mcdf(): Cumulative distribution of the exceedances of a time series
mde_rain mde_rain_monthly: Rainfall in Medellín, Colombia
mlb_diffs: Differences between leagues in Major League Baseball
new_mod_cpt() validate_mod_cpt() mod_cpt(): Base class for changepoint models
model_args(): Retrieve the arguments that a model-fitting function used
model_name(): Retrieve the name of the model that a segmenter or model used
model_variance(): Compute model variance
pad_tau() unpad_tau() is_valid_tau() regions_tau() validate_tau(): Pad and unpad changepoint sets with boundary points
plot(<tidyga>): Plot GA information
plot_best_chromosome() plot_cpt_repeated(): Diagnostic plots for seg_basket objects
plot_intensity(): Plot the intensity of an NHPP fit
regions(): Extract the regions from a tidycpt object
new_seg_basket() seg_basket(): Default class for candidate changepoint sets
new_seg_cpt() seg_cpt(): Base class for segmenters
seg_params(): Retrieve parameters from a segmenter
segment(): Segment a time series using a variety of algorithms
segment_cptga(): Segment a time series using a genetic algorithm
segment_ga() segment_ga_shi() segment_ga_coen() segment_ga_random(): Segment a time series using a genetic algorithm
segment_manual(): Manually segment a time series
segment_pelt(): Segment a time series using the PELT algorithm
tau2time() time2tau(): Convert changepoint sets to time indices
tbl_coef(): Format the coefficients from a linear model as a tibble
test_set(): Simulate time series with known changepoint sets
tidycpt-class: Container class for tidycpt objects
whomademe(): Recover the function that created a model