Method 1: Hybrids as an independent population | Method 2: GCA/SCA model for hybrids | Method 3: grid search

Introduction | Example: Simple Linear Regression | Exercises | Data exploration | Running the Gibbs Sampler | Reducing the autocorrelation by mean-centering the covariate | INLA | Example: Fake News | Example: Emergency Room Complaints | Fitting Bayesian Poisson Regression Models | Bayesian Hierarchical Modelling | Linear Mixed Models | Multilevel Modelling | Generalised Linear Mixed Models | Poisson regression

Fitting a Bayesian polynomial model | Introduction | Fitting a (frequentist) quadratic regression model | Fitting a Bayesian quadratic regression model | Time to individual work

Software for Bayesian Statistical Analysis | BayesX | Other Bayesian Software | Bayesian Logistic Regression | Model Formulation | Example: Fake News | Fitting Bayesian Logistic Regression Models | Model Fitting | Exercises | Bayesian Poisson Regression | Example: Emergency Room Complaints | Fitting Bayesian Poisson Regression Models

Introduction | Bayesian inference for the expected number of u's in a page of A Game of Thrones | The experiment: Number of u's in a page of A Game of Thrones | The sampling model is approximately Poisson | A prior distribution for $\lambda$ | The likelihood function of $\lambda$ | The posterior distribution of $\lambda$ | The posterior predictive distribution for the number of u's in a new page of A Game of Thrones | Time to individual work

How tall the VIBASS' participants are? | Bayesian inference for the mean height of the women VIBASS' participants. The variance of the sampling Normal model is known. | The data | The sampling model is approximately Normal | A prior distribution for $\mu$ | The likelihood function of $\mu$ | The posterior distribution of $\mu$ | The posterior predictive distribution for the height of a new VIBASS participant | Bayesian inference for the mean height of the women VIBASS participants. The variance of the sampling Normal model is unknown. | A prior distribution for $(\mu,, \sigma^2)$ | The likelihood function of $(\mu,, \sigma^2)$ | The posterior distribution of $(\mu,, \sigma^2)$ | Time to individual work

Simulation-based inference in a Bayesian quadratic model | Introduction | Posterior distribution for the model parameters | Analytical inference | Simulation-based inference | Predictive inference | Time to individual work

Introducing M$&$M's | Bayesian inference for the proportion of red M$&$M's | The experiment: Counting red M$&$M's | The sampling model is Binomial | A prior distribution for $\theta$ | The likelihood function of $\theta$ | The posterior distribution of $\theta$ | The posterior predictive distribution for the results of a new experiment | Time for individual work

Bayesian Hierarchical Modelling | Linear Mixed Models | Multilevel Modelling | Exercises | Generalised Linear Mixed Models | Poisson regression | Further Extensions | Final Exercises (Optional!!)

Introduction | Importance Sampling | The Metropolis-Hastings Algorithm | Example: Poisson-Gamma Model | Importance sampling | Metropolis-Hastings | Exercises | Performance of the proposal distribution | Changing the proposal distribution - Importance Sampling | Changing the prior distribution - Metropolis-Hastings | Gibbs Sampling | Example: Simple Linear Regression | Data exploration | Running the Gibbs Sampler | Reducing the autocorrelation by mean-centering the covariate

What is a pedigree | Checking pedigrees | Building pedigrees | Using a pedigree in an additive genetic effect | Recovering Breeding Values in the original coding | Recovering Breeding Values for the founders, in the original coding | Identifying original codes from internal representation

Intro | What is breedR | Installation | Where to find help | License | Roadmap | Future developments | Functionality | Inference | Frequentist | Bayesian | Linear Mixed Models with unstructured random effects | Example dataset | A simple Provenance Test | Initial variances specification | Exploring the results | Further extractor functions | Hierarchical and Factorial models | Model specification | Interactions | Exercise | Hierarchical and Factorial models | Hierarchical and Factorial models #1 | Fitting models | Hierarchical and Factorial models #2 | Hierarchical model | Hierarchical and Factorial models #3 | Factorial model | Additive Genetic Effect | What is an additive genetic effect | Specifying a pedigree | Fitting an animal model | Animal model: results | Extracting Predicted Breeding Values | Handling pedigrees | Spatial autocorrelation | What is spatial autocorrelation | Diagnosing spatial autocorrelation | residuals spatial plot | Diagnosing spatial autocorrelation | variograms of residuals | Interpreting the variograms | Accounting for spatial autocorrelation | The blocks model | Animal-spatial model: results | Variogram of residuals | B-Splines model | Autoregressive model | Change in model residuals | Comparison of spatial components | Prediction of the spatial effect in unobserved locations | Spatial parameters | Number of knots of a splines model | Spatial parameters | Autoregressive parameters of a AR model | Exercise | Tuning spatial parameters | Spatial #1 | B-splines model with increased nok | Spatial #2 | Visualize log-likelihoods | Spatial #3 | Refine grid | Competition | Theoretical model | Permanent Environmental Effect (pec) | Simulation of data | Fitting a competition model | True vs. estimated parameters | Exercise | Competition models | Competition #1 | True vs. predicted components | Competition #2 | Map of residuals and their variogram | Generic component | The Generic model | Implementation of the generic component | Example of result | Prediction | Predicting values for unobserved trees | Leave-one-out cross-validation | Exercise | Cross validation | Cross-validation #1 | Include prediction with full data | Cross-validation #2 | Perform cross-validation on 1/10th of the observations | Cross-validation #3 | MSE of Prediction | Multiple traits | Initial (co)variance specification | Some more features | Metagene interface | Simulation framework | Remote computation | Package options

Missing response | Missing value for a fixed effect | Missing value for a random effect | Missing values in genetic effects | Missing values in coordinates of spatial effects

Using weights | Estimating residual variance heterogeneity

Introduction | Case 1: Explicit genetic component with method = 'ai' | Case 2: Using custom heritability formulae | 2.1 Specifying and explicit function of the variance components | 2.2 Using the Delta Method | Case 3: Using Bootstrap estimation | References

Method 1: using unstructured random effects | Method 2: using the implicit pedigree | Final remarks