A B C D E F H I L M N P R S T U V W
| as.model | Convert, retrieve, or verify a model object | 
| as.model.default | Convert, retrieve, or verify a model object | 
| as.model.tidycpt | Convert, retrieve, or verify a model object | 
| as.segmenter | Convert, retrieve, or verify a segmenter object | 
| as.segmenter.tidycpt | Convert, retrieve, or verify a segmenter object | 
| as.seg_cpt | Convert, retrieve, or verify a segmenter object | 
| as.seg_cpt.cpt | Convert, retrieve, or verify a segmenter object | 
| as.seg_cpt.ga | Convert, retrieve, or verify a segmenter object | 
| as.seg_cpt.seg_basket | Convert, retrieve, or verify a segmenter object | 
| as.seg_cpt.seg_cpt | Convert, retrieve, or verify a segmenter object | 
| as.seg_cpt.wbs | Convert, retrieve, or verify a segmenter object | 
| as_year | Convert a date into a year | 
| binary2tau | Convert changepoint sets to binary strings | 
| BMDL | Bayesian Maximum Descriptive Length | 
| BMDL.default | Bayesian Maximum Descriptive Length | 
| BMDL.nhpp | Bayesian Maximum Descriptive Length | 
| bogota_pm | Particulate matter in Bogotá, Colombia | 
| build_gabin_population | Initialize populations in genetic algorithms | 
| CET | Hadley Centre Central England Temperature | 
| changepoints | Extract changepoints | 
| changepoints.cpt | Extract changepoints | 
| changepoints.default | Extract changepoints | 
| changepoints.ga | Extract changepoints | 
| changepoints.mod_cpt | Extract changepoints | 
| changepoints.seg_basket | Extract changepoints | 
| changepoints.seg_cpt | Extract changepoints | 
| changepoints.tidycpt | Extract changepoints | 
| changepoints.wbs | Extract changepoints | 
| compare_algorithms | Compare various models or algorithms for a given changepoint set | 
| compare_models | Compare various models or algorithms for a given changepoint set | 
| cut_by_tau | Use a changepoint set to break a time series into regions | 
| DataCPSim | Simulated time series data | 
| deg_free | Retrieve the degrees of freedom from a 'logLik' object | 
| diagnose | Diagnose the fit of a segmented time series | 
| diagnose.mod_cpt | Diagnose the fit of a segmented time series | 
| diagnose.nhpp | Diagnose the fit of a segmented time series | 
| diagnose.seg_basket | Diagnose the fit of a segmented time series | 
| diagnose.tidycpt | Diagnose the fit of a segmented time series | 
| exceedances | Compute exceedances of a threshold for a time series | 
| exceedances.default | Compute exceedances of a threshold for a time series | 
| exceedances.double | Compute exceedances of a threshold for a time series | 
| exceedances.nhpp | Compute exceedances of a threshold for a time series | 
| exceedances.ts | Compute exceedances of a threshold for a time series | 
| file_name | Obtain a descriptive filename for a tidycpt object | 
| fitness | Retrieve the optimal fitness (or objective function) value used by an algorithm | 
| fitness.cpt | Retrieve the optimal fitness (or objective function) value used by an algorithm | 
| fitness.ga | Retrieve the optimal fitness (or objective function) value used by an algorithm | 
| fitness.seg_basket | Retrieve the optimal fitness (or objective function) value used by an algorithm | 
| fitness.seg_cpt | Retrieve the optimal fitness (or objective function) value used by an algorithm | 
| fitness.tidycpt | Retrieve the optimal fitness (or objective function) value used by an algorithm | 
| fitness.wbs | Retrieve the optimal fitness (or objective function) value used by an algorithm | 
| fit_lmshift | Regression-based model fitting | 
| fit_lmshift_ar1 | Regression-based model fitting | 
| fit_meanshift | Fast implementation of meanshift model | 
| fit_meanshift_lnorm | Fast implementation of meanshift model | 
| fit_meanshift_norm | Fast implementation of meanshift model | 
| 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. | 
| fit_trendshift | Regression-based model fitting | 
| fit_trendshift_ar1 | Regression-based model fitting | 
| fun_cpt | Class for model-fitting functions | 
| HQC | Hannan–Quinn information criterion | 
| HQC.default | Hannan–Quinn information criterion | 
| HQC.logLik | Hannan–Quinn information criterion | 
| is_model | Convert, retrieve, or verify a model object | 
| is_segmenter | Convert, retrieve, or verify a segmenter object | 
| is_valid_tau | Pad and unpad changepoint sets with boundary points | 
| italy_grads | Italian University graduates by disciplinary groups from 1926-2013 | 
| iweibull | Weibull distribution functions | 
| log_gabin_population | Initialize populations in genetic algorithms | 
| ls_coverage | Algorithmic coverage through tidychangepoint | 
| ls_cpt_penalties | Algorithmic coverage through tidychangepoint | 
| ls_methods | Algorithmic coverage through tidychangepoint | 
| ls_models | Algorithmic coverage through tidychangepoint | 
| ls_penalties | Algorithmic coverage through tidychangepoint | 
| ls_pkgs | Algorithmic coverage through tidychangepoint | 
| MBIC | Modified Bayesian Information Criterion | 
| MBIC.default | Modified Bayesian Information Criterion | 
| MBIC.logLik | Modified Bayesian Information Criterion | 
| mcdf | Cumulative distribution of the exceedances of a time series | 
| mde_rain | Rainfall in Medellín, Colombia | 
| mde_rain_monthly | Rainfall in Medellín, Colombia | 
| MDL | Maximum Descriptive Length | 
| MDL.default | Maximum Descriptive Length | 
| MDL.logLik | Maximum Descriptive Length | 
| mlb_diffs | Differences between leagues in Major League Baseball | 
| model_args | Retrieve the arguments that a model-fitting function used | 
| model_args.cpt | Retrieve the arguments that a model-fitting function used | 
| model_args.default | Retrieve the arguments that a model-fitting function used | 
| model_args.ga | Retrieve the arguments that a model-fitting function used | 
| model_args.seg_cpt | Retrieve the arguments that a model-fitting function used | 
| model_args.wbs | Retrieve the arguments that a model-fitting function used | 
| model_name | Retrieve the name of the model that a segmenter or model used | 
| model_name.character | Retrieve the name of the model that a segmenter or model used | 
| model_name.cpt | Retrieve the name of the model that a segmenter or model used | 
| model_name.default | Retrieve the name of the model that a segmenter or model used | 
| model_name.ga | Retrieve the name of the model that a segmenter or model used | 
| model_name.mod_cpt | Retrieve the name of the model that a segmenter or model used | 
| model_name.seg_basket | Retrieve the name of the model that a segmenter or model used | 
| model_name.seg_cpt | Retrieve the name of the model that a segmenter or model used | 
| model_name.tidycpt | Retrieve the name of the model that a segmenter or model used | 
| model_name.wbs | Retrieve the name of the model that a segmenter or model used | 
| model_variance | Compute model variance | 
| mod_cpt | Base class for changepoint models | 
| mweibull | Weibull distribution functions | 
| new_fun_cpt | Class for model-fitting functions | 
| new_mod_cpt | Base class for changepoint models | 
| new_seg_basket | Default class for candidate changepoint sets | 
| new_seg_cpt | Base class for segmenters | 
| pad_tau | Pad and unpad changepoint sets with boundary points | 
| parameters_weibull | Weibull distribution functions | 
| plot.tidyga | Plot GA information | 
| plot_best_chromosome | Diagnostic plots for 'seg_basket' objects | 
| 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 | 
| regions.mod_cpt | Extract the regions from a tidycpt object | 
| regions.tidycpt | Extract the regions from a tidycpt object | 
| regions_tau | Pad and unpad changepoint sets with boundary points | 
| rlnorm_ts_1 | Simulated time series data | 
| rlnorm_ts_2 | Simulated time series data | 
| rlnorm_ts_3 | Simulated time series data | 
| segment | Segment a time series using a variety of algorithms | 
| segment.numeric | Segment a time series using a variety of algorithms | 
| segment.tbl_ts | Segment a time series using a variety of algorithms | 
| segment.ts | Segment a time series using a variety of algorithms | 
| segment.xts | Segment a time series using a variety of algorithms | 
| segment_ga | Segment a time series using a genetic algorithm | 
| segment_ga_coen | Segment a time series using a genetic algorithm | 
| segment_ga_random | Segment a time series using a genetic algorithm | 
| segment_ga_shi | 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 | 
| seg_basket | Default class for candidate changepoint sets | 
| seg_cpt | Base class for segmenters | 
| seg_params | Retrieve parameters from a segmenter | 
| seg_params.cpt | Retrieve parameters from a segmenter | 
| seg_params.ga | Retrieve parameters from a segmenter | 
| seg_params.seg_cpt | Retrieve parameters from a segmenter | 
| seg_params.wbs | Retrieve parameters from a segmenter | 
| SIC | Schwarz information criterion | 
| split_by_tau | Use a changepoint set to break a time series into regions | 
| tau2binary | Convert changepoint sets to binary strings | 
| tau2time | 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 | 
| time2tau | Convert changepoint sets to time indices | 
| unpad_tau | Pad and unpad changepoint sets with boundary points | 
| validate_fun_cpt | Class for model-fitting functions | 
| validate_mod_cpt | Base class for changepoint models | 
| validate_tau | Pad and unpad changepoint sets with boundary points | 
| whomademe | Recover the function that created a model |