Alluvial Plots in 'ggplot2'

Alluvial plots use variable-width ribbons and stacked bar plots to represent multi-dimensional or repeated-measures data with categorical or ordinal variables; see Riehmann, Hanfler, and Froehlich (2005) and Rosvall and Bergstrom (2010) . Alluvial plots are statistical graphics in the sense of Wilkinson (2006) ; they share elements with Sankey diagrams and parallel sets plots but are uniquely determined from the data and a small set of parameters. This package extends Wickham's (2010) layered grammar of graphics to generate alluvial plots from tidy data.

Generalized Estimation Equation Solver

Generalized Estimation Equation solver.

Fast, Dependency-Free Geodesic Distance Calculations

Dependency-free, ultra fast calculation of geodesic distances. Includes the reference nanometre-accuracy geodesic distances of Karney (2013) , as used by the 'sf' package, as well as Haversine and Vincenty distances. Default distance measure is the "Mapbox cheap ruler" which is generally more accurate than Haversine or Vincenty for distances out to a few hundred kilometres, and is considerably faster. The main function accepts one or two inputs in almost any generic rectangular form, and returns either matrices of pairwise distances, or vectors of sequential distances.

Markov Chain Monte Carlo (MCMC) Package

Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.

Quantile Regression

Estimation and inference methods for models for conditional quantile functions: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press, and Koenker, R. et al. (2017) Handbook of Quantile Regression, CRC Press, .

Mixed FLP and ML Estimation of ETAS Space-Time Point Processes for Earthquake Description

Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; in version 2.3.0 improved update method. Chiodi, Adelfio (2017).

Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis

Provides functions for the Bayesian analysis of extreme value models. The 'rust' package < https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. The functionality of 'revdbayes' is similar to the 'evdbayes' package < https://cran.r-project.org/package=evdbayes>, which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. In addition, there are functions for making inferences about the extremal index, using the models for threshold inter-exceedance times of Suveges and Davison (2010) and Holesovsky and Fusek (2020) . Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero.

Functions for Drawing Ellipses and Ellipse-Like Confidence Regions

Contains various routines for drawing ellipses and ellipse-like confidence regions, implementing the plots described in Murdoch and Chow (1996, ). There are also routines implementing the profile plots described in Bates and Watts (1988, ).

Improved Predictors

Improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.

Helpers for Model Coefficients Tibbles

Provides suite of functions to work with regression model 'broom::tidy()' tibbles. The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more.