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 Bogot<c3><a1>, 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<e2><80><93>Quinn information criterion |
HQC.default | Hannan<e2><80><93>Quinn information criterion |
HQC.logLik | Hannan<e2><80><93>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 Medell<c3><ad>n, Colombia |
mde_rain_monthly | Rainfall in Medell<c3><ad>n, 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 |