Wishart Distribution | Inverse-Wishart Distribution | Properties | Matrix-Normal Distribution | Matrix-Normal Inverse-Wishart Distribution | Matrix-t Distribution | Random-Effects Normal Distribution | Hierarchical Normal-Normal Model

Overview | Data generating model | Local likelihood estimation of the dependence parameter | Copula Selection and Model Tuning | Sensitivity to the choice of copula family | References

Pre-Processing | Two-Population Comparisons | Multinomial Model | Hypothesis Testing | Pairwise Comparisons between Multiple Populations | Parameter Estimation | Bayesian Hypothesis Testing | MCMC Sampling from the Posterior Distribution | Calculating the Posterior P-Value | Future Work | References

Introduction | Example 1: Nonlinear Regression with Correlated Errors | Profile Likelihood | Example 2: Generalized Cox-Ingersoll-Ross Process | Bayesian Inference | Appendix: Conjugate Posteriors for LMNs | References

Heston's stochastic volatility model | Bivariate Ornstein-Uhlenbeck process | Lotka-Volterra predator-prey model | Prokaryotic autoregulatory gene network model | References

Introduction | Creating an sde.model object | The sdeModel class definition | Compiling and checking the sde.model object | Simulating trajectories from the Lotka-Volterra model | Inference for multivariate SDE models | Missing data specification with sde.init() | Default prior specification | Default prior | Custom prior specification | The sdePrior class definition | Formatting the R input to the C++ code | Compiling and checking the prior | Installation | Enable C++ compiling for R | Optimize settings for the C++ compiler (optional) | Enable OpenMP support (optional) | References

1. Preliminaries | 2. Formatting data for use in nicheROVER | 3. Generate the posterior distributions of $\mu$ (mean) and $\Sigma$ (variance) for isotope values for each species with the default prior | 4. Create 2-d projections of a subset of niche regions | 5. Calculate and display niche overlap estimates | 6. Calculate and display niche size estimates

Simulation of Fractional Brownian Motion | Inference for the Hurst Parameter | The Toeplitz Matrix Class | Maximum Likelihood Calculation | Caution with R6 Classes | Superfast Newton-Raphson | References

Introduction | Example: Quadratic Objective Function | Visual Checks with optim_proj() | Quantification of Projection Plots | optim_refit(): A Numerical Alternative to Projection Plots | Future Work: Constrained Optimization

Motivation | Walkthrough | Installation | Doxygen Markup | Processing with rdoxygen | Compatibility with devtools