| air_quality | Air quality data for 12 Beijing monitoring stations from 2013 to 2017 |
| augment.Pca | Augment data with results from a robust principal component analysis |
| biplot_projection | Biplot of a two-dimensional projection |
| chauvenet_anomalies | Anomalies according to Peirce's and Chauvenet's criteria |
| cricket_batting | Cricket batting data for international test players |
| density_df | Convert distributional object to a data frame |
| dist_density | Create distributional object based on a specified density |
| dist_kde | Create distributional object based on a kernel density estimate |
| dist_mclust | Convert Gaussian mixture model to a distributional object |
| dixon_anomalies | Statistical tests for anomalies using Grubbs' test and Dixon's test |
| fetch_air_quality | Air quality data for 12 Beijing monitoring stations from 2013 to 2017 |
| fetch_wine_reviews | Wine prices and points |
| fr_mortality | French mortality rates by age and sex |
| gg_bagplot | Bagplot |
| gg_density | Produce ggplot of densities from distributional objects in 1 or 2 dimensions |
| gg_hdrboxplot | HDR plot |
| glosh_scores | GLOSH scores |
| grubbs_anomalies | Statistical tests for anomalies using Grubbs' test and Dixon's test |
| gun_deaths | Gun ownership and homicide rates by country |
| hampel_anomalies | Identify anomalies using the Hampel filter |
| hdr_table | Table of Highest Density Regions |
| kde_bandwidth | Robust bandwidth estimation for kernel density estimation |
| lof_scores | Local outlier factors |
| mvscale | Compute robust multivariate scaled data |
| n01 | Multivariate standard normal data |
| oldfaithful | Old faithful eruption data |
| outlier_map | Outlier map from a projection or principal component analysis |
| peirce_anomalies | Anomalies according to Peirce's and Chauvenet's criteria |
| stray_anomalies | Stray anomalies |
| stray_scores | Stray scores |
| surprisals | Surprisals and surprisal probabilities |
| surprisals.data.frame | Surprisals and surprisal probabilities computed from data |
| surprisals.gam | Surprisals and surprisal probabilities computed from a model |
| surprisals.glm | Surprisals and surprisal probabilities computed from a model |
| surprisals.lm | Surprisals and surprisal probabilities computed from a model |
| surprisals.matrix | Surprisals and surprisal probabilities computed from data |
| surprisals.numeric | Surprisals and surprisal probabilities computed from data |
| surprisals_prob | Surprisals and surprisal probabilities |
| surprisals_prob.data.frame | Surprisals and surprisal probabilities computed from data |
| surprisals_prob.gam | Surprisals and surprisal probabilities computed from a model |
| surprisals_prob.glm | Surprisals and surprisal probabilities computed from a model |
| surprisals_prob.lm | Surprisals and surprisal probabilities computed from a model |
| surprisals_prob.matrix | Surprisals and surprisal probabilities computed from data |
| surprisals_prob.numeric | Surprisals and surprisal probabilities computed from data |
| wine_reviews | Wine prices and points |