## Recently Published

##### Parametric Excess Hazard Estimation: General Hazards

This R code illustrates the use of General Hazard structure models in a simulated data set. The data set was simulated using the General Hazard (GH) structure. The idea is to fit the parametric regression models with hazard structures PH, AH, AFT, and GH and select the one favoured by the Akaike Information Criterion (AIC).

##### Parametric Excess Hazard Estimation: Proportional Hazards

This R code illustrates the use of General Hazard structure models in a simulated data set. The data set was simulated using the Proportional Hazards (PH) structure. The idea is to fit the parametric regression models with hazard structures PH, AH, AFT, and GH and select the one favoured by the Akaike Information Criterion (AIC).

##### Frequentist vs Noninformative Bayesian inference in the Binomial model

Frequentist vs Noninformative Bayesian inference in the Binomial model using Uniform and Jeffreys priors.

##### Visual comparison of two populations

Some visual tools for comparing two univariate samples

##### Flexible linear mixed models: HIV-1 viral load after unstructured treatment interruption

A real data example of linear mixed models for censored responses with flexible random effects and flexible residual errors.

##### Flexible linear mixed models: Framingham study

A real data application of linear mixed models with flexible errors and flexible random effects.

##### How to create a random Secret Santa list in R

Two different methods to create a Secret Santa random list in R from a list of names.

##### An Introduction to MCMC

Illustration of some properties of MCMC samplers

##### Predictive Beta Binomial distribution

The predictive distribution for a Binomial sampling model with Beta prior.

##### The Normal-Normal Bayesian model (known variance)

The posterior distribution of the mean for a normal sampling model with known variance and normal prior distribution

##### The Beta-Binomial model

A short description of the Binomial distribution, the Beta distribution, and the Bayesian Beta-Binomial model.

##### The Inverse Mills Ratio

Some properties of the Inverse Mills Ratio

##### Kernel Density and Distribution Estimation for data with different supports

R codes to implement kernel density and distribution estimators for data with support on R, R_+, and (0,1) by using a transformation approach.

##### Bayesian Variable Selection: Analysis of DLD data

Tractable Bayesian Variable Selection: Beyond normality. Analysis of DLD data using two-piece residual errors and non-local priors.

##### The Generalised Weibull Distribution

Cumulative distribution function, quantile function, hazard function, and cumulative hazard function of the Generalised Weibull distribution.

##### The Exponentiated Weibull distribution

Probability density function, cumulative distribution function, quantile function, random number generation, hazard function, and cumulative hazard function of the Exponentiated Weibull distribution.

##### An objective prior for the number of degrees of freedom of a multivariate t distribution

An objective prior for the number of degrees of freedom of a multivariate t distribution

##### The Jeffreys prior for skew–symmetric models

The Jeffreys prior for the skewness parameter in skew–symmetric models

##### Kullback Leibler divergence between a multivariate t and a multivariate normal distributions

A tractable, scalable, expression for the Kullback Leibler divergence between a multivariate t and a multivariate normal distributions

##### Kullback Leibler divergence between two multivariate t distributions

A tractable, scalable, expression for the Kullback Leibler divergence between two multivariate t distributions

##### An application of an objective prior for the number of degrees of freedom of a multivariate t distribution

A financial application of an objective prior for the number of degrees of freedom of a multivariate t distribution

##### Nonparametric estimation of P(X<Y) for paired data

Several types of Nonparametric estimators of P(X<Y) for paired data

##### Galton’s Forecasting Competition

Galton’s Forecasting Competition data modelling using the DTP R package.

##### A weakly informative prior for the degrees of freedom of the t distribution

Implementation of a weakly informative prior for the degrees of freedom of the t distribution.

##### Mollified two-piece distributions

Mollification of two-piece distributions.

##### The Laplace and two-piece Laplace Distributions

Implementation of the Laplace and two piece Laplace distributions (using the R package twopiece).

##### Natural (non-) informative priors for the skew-normal distribution

Real data example to illustrate the use of Jeffreys and Total Variation priors for the shape parameter of the skew-normal distribution

##### t-copula with t and two piece t marginals

A real data example to illustrate how to fit a t-copula with t and two piece t marginals

##### TPSAS R Package

The TPSAS R package implements the univariate two-piece sinh–arcsinh distribution

##### Sinh-arcsinh distribution

Implementation of the probability density function, cumulative distribution function, quantile function, and random number generation of the SAS distribution.

##### Bayesian inference for the ratio of the means of two normals

Bayesian inference for the ratio of the means of two normal populations with unequal variances using reference priors.

##### Approximate Maximum Likelihood Estimation (AMLE)

A simple approach to maximum intractable likelihood estimation: AMLE. Two toy examples.

##### Ratio of two normals and a normal approximation

Implementaion of the distribution of the ratio of two independent normal distributions and a normal approximation.

##### Posterior QQ envelopes: normality test

Implementation of Posterior QQ envelopes for normality test.

##### Posterior QQ envelopes: Linear regression

Implementation of Posterior QQ envelopes and predictive QQ plots in the context of linear regression.

##### Flexible AFT Models III: Bayesian + two-piece

Bayesian AFT models with two-piece errors

##### Flexible AFT Models II: Bayesian + skew-symmetric

Bayesian Accelerated Failure Time models with skew-symmetric errors.

##### Flexible AFT Models I: MLE + two-piece

Accelerated failure time models with two-piece errors using maximum likelihood estimation.

##### twopiece R package with applications

The twopiece R package implements the family of Two-Piece distributions.

##### DTP R package

The DTP R package implements the family of Double Two-Piece distributions.

##### Two-piece Generalised Hyperbolic distribution

Description and implementation of the two-piece Generalised Hyperbolic distribution.

##### Two-piece Variance Gamma distribution

Implementation and description of the two-piece Variance Gamma distribution.

##### Two-piece Johnson-SU distribution

Implementation and description of the two-piece Johnson-SU distribution