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 |