## Recently Published

##### MEGH: A parametric class of mixed-effects general hazard models

MEGH: A parametric class of mixed-effects general hazard models
Leukemia data set and Survival Models

##### The effect of the shape (skewness) parameter in skew-symmetric models

The effect of the shape (skewness) parameter in skew-symmetric models based on the TV distance

##### Approximating the Beta PDF with a Kumaraswamy PDF based on the TV distance

Approximating the Beta PDF with a Kumaraswamy PDF based on the TV distance

##### The Wasserstein prior for the normal linear regression model

The Wasserstein prior for the normal linear regression model

##### The Wasserstein prior for the Skew Normal distribution: an applicationDocument

The Wasserstein prior for the Skew Normal distribution: an application

##### The Wasserstein prior for the Skew Normal distribution

The Wasserstein prior for the Skew Normal distribution

##### DTP Package

DTP R Package

##### The family of two-piece distributions

This R markdown contains illustrative examples about the family of two-piece distributions and the "twopiece" R package.

##### Simulacrum data: lung cancer and Net Survival GH model

Net survival example using the R package GHSurv

##### Simulacrum data: lung cancer and Overall Survival GH model

Overall survival example using the Simulacrum data set

##### A unifying framework for flexible excess hazard modelling with applications in cancer epidemiology: Leukemia data

A unifying framework for flexible excess hazard modelling with applications in cancer epidemiology: Leukemia data

##### A unifying framework for flexible excess hazard modelling with applications in cancer epidemiology: The Simulacrum

A unifying framework for flexible excess hazard modelling with applications in cancer epidemiology: The Simulacrum

##### Fitting the two-piece normal distribution to the Volcanoes dataset

Fitting the two-piece normal distribution to the Volcanoes dataset using 4 methods.

##### Bayesian survival analysis using a General Hazard Structure

Bayesian survival analysis using a General Hazard Structure

##### Downloading financial data from Yahoo! Finance in R

Using the R packages tidyquant and BatchGetSymbols.

##### 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.

##### Bivariate Distributions with twopiece and double twopiece marginals

Modelling bivariate log returns using a Gaussian copula with twopiece and double twopiece marginals

##### Simulating joint models for longitudinal and survival data

Algorithms and code for simulating from joint models for longitudinal and survival data with time dependent effects.

##### The BTV(1,1) prior for the Power Generalised Weibull Distribution

The BTV(1,1) prior for the Power Generalised Weibull Distribution

##### The Generalised Gamma Distribution

The Generalised Gamma Distribution and a link with the Gamma Distribution

##### Simulation of normal corrrelated covariates: implementation and a warning

Simulation of normal corrrelated covariates: implementation and a warning about the correlation matrix

##### Example: no covariates

Parametric Survival Models

##### Example: Survival Regression Models

Parametric Survival Regression Models

##### Quick parallelisation of a for loop

Quick and dirty parallelisation of a for loop

##### The Double two-piece Sinh-Arcsinh distribution

Implementation of the pdf, cdf, quantile function, and random number generation of the DTP SAS distribution using the DTP R package.

##### Likelihood Ratio Test: Skew Normal vs Normal

Likelihood Ratio Test: Skew Normal vs Normal

##### Bayesian inference on the two-piece normal distribution using rstan

Bayesian inference on the two-piece normal distribution using rstan. Simulated and Real Data applications.

##### The softmax Function

Implementation of the softmax function using the recursive formula.

##### The Hyperbolic Secant Distribution

pdf, the cdf, the quantile function, random number generation, and moments associated to the Hyperbolic Secant distribution.

##### The LogSumExp function

The LogSumExp function is the logarithm of the sum of the exponentials of n values. This R Markdown presents 5 methods to calculate the LogSumExp function.

##### The Power Generalised Weibull Distribution

The Power Generalised Weibull Distribution in R: a three-parameter distribution with positive support and flexible hazard function.

##### The Cox Proportional Hazards Model and the Partial Likelihood function

The Cox Proportional Hazards Model and the Partial Likelihood function

##### The Laplace Inverse Mills Ratio

R code and illustrations of the behaviour of the Laplace Inverse Mills Ratio (LIMR) and its first derivative.

##### Simulating survival times from a General Hazard structure with a flexible baseline hazard

This tutorial shows how to simulate from a General Hazard structure that includes time dependent effects as well as effects that only affect the hazard level.

##### Simulating from a Bivariate Gaussian Copula

R code to simulate from a bivariate distribution based on a Gaussian copula

##### The Kumaraswamy Distribution

pdf, the cdf, the quantile function, random number generation, and moments associated to the Kumaraswamy distribution.

##### Excess hazard models for insufficiently stratified life tables

A simulated data example

##### MLE Logistic Distribution

MLE for the location and scale parameters in the Logistic Distribution

##### t-intervals: unpaired observations

Life spans of wild type vs. transgenic mosquitoes

##### Paired differences: the Darwin data

t-intervals: paired observations

##### How many Monte Carlo simulations to get to an accurate estimate of a proportion?

Estimating a proportion $\theta$ and its relationship with the number of Monte Carlo simulations

##### Permutation test: examples

Some examples of permutation tests using the difference of means and the Kolmogorov-Smirnov test statistic

##### Robust Outlier Detection

Robust Outlier Detection vs. Non-Robust Outlier Detection

##### Robust Estimation of Location

Mean vs. Median

##### Robust Estimation of Scale

Normalised Median Absolute Deviation vs. Standard Deviation

##### Binomial Trial: Bayesian Analysis

A Bayesian Analysis of a Binomial Trial.

##### Wald Confidence Interval for the Normal distribution

Second order approximation: Wald confidence intervals for the Normal distribution

##### A Brief analysis of the Challenger data

Analysis of the Challenger disaster data using logistic regression

##### Profile likelihood confidence intervals for the parameters of the normal distribution

Profile likelihood confidence intervals for the parameters of the normal distribution (mean and standard deviation)

##### Performance of normal Confidence Intervals for log-odds

A simulation study to check the performance of asymptotic normal CIs for the log-odds

##### Method of moments for the Kumaraswamy distribution

An example where the Method of Moments does not lead to a closed form solution and requires the use of numerical methods to obtain a solution to the corresponding estimating equations.

##### Logistic Regression: The Beetle data set

Maximum likelihood estimation in the logistic regression model

##### Numerical calculation of the Wasserstein-1 metric in 1-D: examples

Three examples of the numerical calculation of the Wasserstein-1 metric, including its use for comparing survival curves.

##### 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 Hazards (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.

##### 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.

##### 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