# Tools for Post-Processing Predicted Values

## Documentation for package ‘probably’ version 1.1.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

A B C I L M P R S T W

## -- A --

any_equivocal | Locate equivocal values
append_class_pred | Add a 'class_pred' column
as_class_pred | Coerce to a 'class_pred' object

## -- B --

boosting_predictions | Boosted regression trees predictions
boosting_predictions_oob | Boosted regression trees predictions
boosting_predictions_test | Boosted regression trees predictions
bound_prediction | Truncate a numeric prediction column

## -- C --

cal_apply | Applies a calibration to a set of existing predictions
cal_apply.cal_object | Applies a calibration to a set of existing predictions
cal_apply.data.frame | Applies a calibration to a set of existing predictions
cal_apply.tune_results | Applies a calibration to a set of existing predictions
cal_estimate_beta | Uses a Beta calibration model to calculate new probabilities
cal_estimate_beta.data.frame | Uses a Beta calibration model to calculate new probabilities
cal_estimate_beta.grouped_df | Uses a Beta calibration model to calculate new probabilities
cal_estimate_beta.tune_results | Uses a Beta calibration model to calculate new probabilities
cal_estimate_isotonic | Uses an Isotonic regression model to calibrate model predictions.
cal_estimate_isotonic.data.frame | Uses an Isotonic regression model to calibrate model predictions.
cal_estimate_isotonic.grouped_df | Uses an Isotonic regression model to calibrate model predictions.
cal_estimate_isotonic.tune_results | Uses an Isotonic regression model to calibrate model predictions.
cal_estimate_isotonic_boot | Uses a bootstrapped Isotonic regression model to calibrate probabilities
cal_estimate_isotonic_boot.data.frame | Uses a bootstrapped Isotonic regression model to calibrate probabilities
cal_estimate_isotonic_boot.grouped_df | Uses a bootstrapped Isotonic regression model to calibrate probabilities
cal_estimate_isotonic_boot.tune_results | Uses a bootstrapped Isotonic regression model to calibrate probabilities
cal_estimate_linear | Uses a linear regression model to calibrate numeric predictions
cal_estimate_linear.data.frame | Uses a linear regression model to calibrate numeric predictions
cal_estimate_linear.grouped_df | Uses a linear regression model to calibrate numeric predictions
cal_estimate_linear.tune_results | Uses a linear regression model to calibrate numeric predictions
cal_estimate_logistic | Uses a logistic regression model to calibrate probabilities
cal_estimate_logistic.data.frame | Uses a logistic regression model to calibrate probabilities
cal_estimate_logistic.grouped_df | Uses a logistic regression model to calibrate probabilities
cal_estimate_logistic.tune_results | Uses a logistic regression model to calibrate probabilities
cal_estimate_multinomial | Uses a Multinomial calibration model to calculate new probabilities
cal_estimate_multinomial.data.frame | Uses a Multinomial calibration model to calculate new probabilities
cal_estimate_multinomial.grouped_df | Uses a Multinomial calibration model to calculate new probabilities
cal_estimate_multinomial.tune_results | Uses a Multinomial calibration model to calculate new probabilities
cal_estimate_none | Do not calibrate model predictions.
cal_estimate_none.data.frame | Do not calibrate model predictions.
cal_estimate_none.grouped_df | Do not calibrate model predictions.
cal_estimate_none.tune_results | Do not calibrate model predictions.
cal_plot_breaks | Probability calibration plots via binning
cal_plot_breaks.data.frame | Probability calibration plots via binning
cal_plot_breaks.grouped_df | Probability calibration plots via binning
cal_plot_breaks.tune_results | Probability calibration plots via binning
cal_plot_logistic | Probability calibration plots via logistic regression
cal_plot_logistic.data.frame | Probability calibration plots via logistic regression
cal_plot_logistic.grouped_df | Probability calibration plots via logistic regression
cal_plot_logistic.tune_results | Probability calibration plots via logistic regression
cal_plot_regression | Regression calibration plots
cal_plot_regression.data.frame | Regression calibration plots
cal_plot_regression.grouped_df | Regression calibration plots
cal_plot_regression.tune_results | Regression calibration plots
cal_plot_windowed | Probability calibration plots via moving windows
cal_plot_windowed.data.frame | Probability calibration plots via moving windows
cal_plot_windowed.grouped_df | Probability calibration plots via moving windows
cal_plot_windowed.tune_results | Probability calibration plots via moving windows
cal_validate_beta | Measure performance with and without using Beta calibration
cal_validate_beta.resample_results | Measure performance with and without using Beta calibration
cal_validate_beta.rset | Measure performance with and without using Beta calibration
cal_validate_beta.tune_results | Measure performance with and without using Beta calibration
cal_validate_isotonic | Measure performance with and without using isotonic regression calibration
cal_validate_isotonic.resample_results | Measure performance with and without using isotonic regression calibration
cal_validate_isotonic.rset | Measure performance with and without using isotonic regression calibration
cal_validate_isotonic.tune_results | Measure performance with and without using isotonic regression calibration
cal_validate_isotonic_boot | Measure performance with and without using bagged isotonic regression calibration
cal_validate_isotonic_boot.resample_results | Measure performance with and without using bagged isotonic regression calibration
cal_validate_isotonic_boot.rset | Measure performance with and without using bagged isotonic regression calibration
cal_validate_isotonic_boot.tune_results | Measure performance with and without using bagged isotonic regression calibration
cal_validate_linear | Measure performance with and without using linear regression calibration
cal_validate_linear.resample_results | Measure performance with and without using linear regression calibration
cal_validate_linear.rset | Measure performance with and without using linear regression calibration
cal_validate_logistic | Measure performance with and without using logistic calibration
cal_validate_logistic.resample_results | Measure performance with and without using logistic calibration
cal_validate_logistic.rset | Measure performance with and without using logistic calibration
cal_validate_logistic.tune_results | Measure performance with and without using logistic calibration
cal_validate_multinomial | Measure performance with and without using multinomial calibration
cal_validate_multinomial.resample_results | Measure performance with and without using multinomial calibration
cal_validate_multinomial.rset | Measure performance with and without using multinomial calibration
cal_validate_multinomial.tune_results | Measure performance with and without using multinomial calibration
cal_validate_none | Measure performance without using calibration
cal_validate_none.resample_results | Measure performance without using calibration
cal_validate_none.rset | Measure performance without using calibration
cal_validate_none.tune_results | Measure performance without using calibration
class_pred | Create a class prediction object
collect_metrics.cal_rset | Obtain and format metrics produced by calibration validation
collect_predictions.cal_rset | Obtain and format predictions produced by calibration validation
control_conformal_full | Controlling the numeric details for conformal inference

## -- I --

int_conformal_cv | Prediction intervals via conformal inference CV+
int_conformal_cv.default | Prediction intervals via conformal inference CV+
int_conformal_cv.resample_results | Prediction intervals via conformal inference CV+
int_conformal_cv.tune_results | Prediction intervals via conformal inference CV+
int_conformal_full | Prediction intervals via conformal inference
int_conformal_full.default | Prediction intervals via conformal inference
int_conformal_full.workflow | Prediction intervals via conformal inference
int_conformal_quantile | Prediction intervals via conformal inference and quantile regression
int_conformal_quantile.workflow | Prediction intervals via conformal inference and quantile regression
int_conformal_split | Prediction intervals via split conformal inference
int_conformal_split.default | Prediction intervals via split conformal inference
int_conformal_split.workflow | Prediction intervals via split conformal inference
is_class_pred | Test if an object inherits from 'class_pred'
is_equivocal | Locate equivocal values

## -- L --

levels.class_pred | Extract 'class_pred' levels
locate-equivocal | Locate equivocal values

## -- M --

make_class_pred | Create a 'class_pred' vector from class probabilities
make_two_class_pred | Create a 'class_pred' vector from class probabilities

## -- P --

predict.int_conformal_cv | Prediction intervals from conformal methods
predict.int_conformal_full | Prediction intervals from conformal methods
predict.int_conformal_quantile | Prediction intervals from conformal methods
predict.int_conformal_split | Prediction intervals from conformal methods

## -- R --

reportable_rate | Calculate the reportable rate

## -- S --

segment_logistic | Image segmentation predictions
segment_naive_bayes | Image segmentation predictions
species_probs | Predictions on animal species

## -- T --

threshold_perf | Generate performance metrics across probability thresholds
threshold_perf.data.frame | Generate performance metrics across probability thresholds

## -- W --

which_equivocal | Locate equivocal values