 # FJRubio

## Recently Published ##### The Hyperbolic Secant Distribution
pdf, the cdf, the quantile function, random number generation, and moments associated to the Hyperbolic Secant distribution. ##### The family of two-piece distributions
This R markdown contains illustrative examples about the family of two-piece distributions and the "twopiece" R package. ##### 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. ##### 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. ##### The Kumaraswamy Distribution
pdf, the cdf, the quantile function, random number generation, and moments associated to the Kumaraswamy distribution. ##### Likelihood Ratio Test: Skew Normal vs Normal
Description and implementation of the Likelihood Ratio Test for testing Normality vs skew-Normality. It also includes a simulation stud of the size and the power of the test when the sample size is 500. This simulation study shows that the LRT performs poorly (low power and wrong size) for this sample size, unless the null hypothesis is clearly false. ##### 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 ##### 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. ##### 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. ##### 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. ##### 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 ##### 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 ##### 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 ##### 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. ##### 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 ##### 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. ##### 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: Linear regression
Implementation of Posterior QQ envelopes and predictive QQ plots in the context of linear regression.