Installation | Illustrative Data Analysis with Core Functionality | Loading the Data | Common Convention Across the BRcal Package | Assessing Calibration via bayes_ms() and llo_lrt() | Bayesian Model Selection-based approach (bayes_ms()) | Setting the prior model probabilities via Pmc | Likelihood Ratio Test for Calibration (llo_lrt()) | MLE Recalibration | Specifying probs_only = TRUE | Visualizing MLE-recalibrated Probabilities via lineplot() | Boldness-Recalibration via brcal() | Visualizing Boldness-Recalibrated Probabilities via lineplot() | Using tau = TRUE | Additional Features and Details | Passing Additional / Different Arguments to nloptr() | Start Values | Algorithm | Bounds | Stopping criteria | Suppressing output from nloptr() | Passing Additional / Different Arguments to optim() | Conventions for Passing Arguments to optim() | Stopping Criteria | Algorithm and Bounds | Suppressing the output from optim() | Passing Arguments via a List | Linear in Log Odds (LLO) function LLO() | Using LLO() with MLEs or Boldness-Recalibration Estimates | Extreme Value of delta or gamma | Specifying epsilon | Specifying event | Visualizing Posterior Model Probability as a Function of $\delta$ and $\gamma$ via plot_params() | Setting bounds and grid density | Saving and Reusing z | Adding contours and using countors_only=TRUE | Passing Additional / Different Arguments to image.plot() and contour() | Visualizing Recalibrated Probability Forecasts via lineplot() | Specifying Levels of Boldness-Recalibration via t_levels | Saving and reusing df | Omitting the Original or MLE-Recalibrated sets from plotting via plot_original and plot_MLE | Cosmetic Adjustments | Thinning | Passing Additional Arguments to optim and nloptr | References