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, .

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.

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).

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.

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, ).

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.

Extra Analysis Results Data Utilities

Create extra Analysis Results Data (ARD) summary objects. The package supplements the simple ARD functions from the 'cards' package, exporting functions to put statistical results in the ARD format. These objects are used and re-used to construct summary tables, visualizations, and written reports.

Path Diagrams and Visual Analysis of Various SEM Packages' Output

Path diagrams and visual analysis of various SEM packages' output.

Derivative-Free Optimization Algorithms by Quadratic Approximation

Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell.

Detection of Univariate Outliers

Provides well-known techniques for detecting univariate outliers. Methods for handling skewed distributions are included. The Hidiroglou-Berthelot (1986) method for detecting outliers in ratios of historical data is also implemented. When available, survey weights can be incorporated in the detection process.