Introduction | Setup | Anatomy of geom_dotsinterval() | Controlling dot layout | Side | Layout | Beeswarm plots | Varying color, fill, shape, and linewidth | Varying discrete aesthetics within dot groups | Varying continuous aesthetics within dot groups | Constraining dot size | On discrete distributions | On analytical distributions | Varying continuous aesthetics with analytical distributions | Thresholds | Rain cloud plots | Dotplots with Monte Carlo Standard Error | Logit dotplots

Introduction | Setup | Point estimates | For a fit line

Introduction | Setup | The lineribbon family | Lineribbons on already-summarized data | Lineribbons on sample data | Lineribbon "gradients" | Lineribbon density gradients | Multiple lineribbons on one plot | Lineribbons on analytical distributions | More examples | Curve boxplots (aka lineribbons with joint intervals or curvewise intervals) | Limitations of curvewise intervals

Introduction | Setup | Roadmap: The slabinterval meta-geometry | Eye plots and half-eye plots | On sample data | On analytical distributions | Visualizing frequentist uncertainty | Visualizing priors | Sharing thickness scaling across geometries | Scale transformations of densities | Summing up eye plots: stat_[half]eye | Histogram + interval plots | Histograms of analytical distributions | CCDF bar plots | Summing up CDF bar plots | Gradient plots | Avoiding "choppy"-looking gradients | Dotplots | Quantile dotplots | Custom plots | Gradients of alpha, color, and fill | CCDF Gradients | Highlighting and other combinations | Mashups with Correll and Gleicher-style gradients | Densities filled according to intervals | Annotating slabs with spikes | Using color ramps for fill and color aesthetics | Raindrop plots | Creating ridge plots | Varying side, scale, and justification within geoms | Multiple slabs and intervals in composite plots

Introduction | Setup | Example dataset | Model | Extracting draws from a fit in tidy-format using spread_draws | Gathering variable indices into a separate column in a tidy format data frame | Point summaries and intervals | With simple model variables | With indexed model variables | Combining variables with different indices in a single tidy format data frame | Plotting intervals with multiple probability levels | Intervals with densities | Other visualizations of distributions: stat_slabinterval | Posterior means and predictions | Quantile dotplots | Posterior predictions | Posterior predictions, Kruschke-style | Fit/prediction curves | Extracting distributional regression parameters | Comparing levels of a factor | Ordinal models | Ordinal model with continuous predictor | Ordinal model with categorical predictor

Introduction | Setup | Example dataset | Model | Extracting draws from a fit in tidy-format using spread_draws | Gathering variable indices into a separate column in a tidy format data frame | Point summaries and intervals | With simple model variables | With indexed variables | Combining variables with different indices in a single tidy format data frame | Plotting intervals with multiple probability levels | Intervals with densities | Other visualizations of distributions: stat_slabinterval | Posterior means and predictions | Quantile dotplots | Posterior predictions | Fit/prediction curves | Comparing levels of a factor | Ordinal models | Ordinal model with categorical predictor

Introduction | Setup | Example dataset | Model for y_star | Residuals | Probability residuals and quantile residuals | Basic interval-censored model | Randomized quantile residuals | What if the model does not fit well? | Model | Robust interval-censored model | Ordinal regression | Randomized quantile residuals for a discrete distribution | A function for probability residuals | Logistic regression

Introduction | Philosophy | Supported model types | Setup | Example dataset | Using compose_data to prepare a data frame for the model | Extracting draws from a fit in tidy-format using spread_draws | Extracting model variable indices into a separate column in a tidy format data frame | Automatically converting columns and indices back into their original data types | Point summaries and intervals with the point_interval functions: [median|mean|mode]_[qi|hdi] | With simple variables, wide format | With indexed variables | Plotting points and intervals | Using geom_pointinterval | Intervals with posterior violins ("eye plots"): stat_eye | Other visualizations of distributions: stat_slabinterval | Intervals with multiple probability levels: the .width = argument | Plotting posteriors as quantile dotplots | Alternative point summaries and intervals: median, mean, mode; qi, hdi, hdci | Combining variables with different indices in a single tidy format data frame | Posterior predictions | Posterior predictions with posterior distributions of means | Comparing levels of a factor | Gathering all model variable names into a single column: gather_draws and gather_variables | Selecting variables using regular expressions | Drawing fit curves with uncertainty | Compatibility with other packages | Compatibility of point_interval with broom::tidy: A model comparison example | Compatibility with bayesplot using unspread_draws and ungather_draws | Compatibility with emmeans (formerly lsmeans) | Using rstanarm or brms | Using MCMCglmm

Introduction | Setup | Example dataset | Model | Extracting draws from a fit in tidy-format using spread_rvars | Gathering variable indices into a separate column in a tidy format data frame | Point summaries and intervals | With simple model variables | With indexed model variables | Combining variables with different indices in a single tidy format data frame | Plotting point summaries and intervals | Intervals with densities | Other visualizations of distributions: stat_slabinterval | Posterior means | Quantile dotplots | Posterior predictions | Posterior predictions, Kruschke-style | Fit/prediction curves | Extracting distributional regression parameters | Comparing levels of a factor | Ordinal models | Ordinal model with continuous predictor

Introduction | Contents | Libraries needed for this vignette | Test dataset | Contrast tests of main effects | Tests of differences in pairwise combinations of levels between factors in interactions | Tests of differences of differences in interactions

Introduction | Contents | Libraries needed for this | Test dataset | Partial eta-squared | Cohen's d | in the linear model (for comparison) | in ART | Confidence intervals