Introduction | Brownian motion with drift | A PCMBase class for Brownian motion with drift | Example run | References

Data | Models | The $\mathcal{G}_{LInv}$-family of models | Example: Ornstein-Uhlenbeck model types | Groups of model types | Creating PCM objects | Model regimes | Printing models in the form of a table | Mixed Gaussian models | Simulating data on a phylogenetic tree | Calculating likelihoods | References

Introduction | Generic functions for PCM parameters | Parameter S3 class | Main type | Scope | Omission | Constancy | Transformation | _CholeskyFactor | _Schur | Other restrictions | Generating model parametrizations | References

Example tree and data | A mixed Gaussian model | Likelihood values for the three example variants | Tracing the likelihood calculation using the function PCMLikTrace | Variant 1 | Variant 2 | Variant 3 | A step by step description of the log-likelihood calculation | Step 1: Calculating $\vec | Calculating $\vec{\omega}$, $\mathbf{\Phi}$ and $\mathbf{V}$ for a node in an OU regime | Calculating $\vec{\omega}$, $\mathbf{\Phi}$ and $\mathbf{V}$ for a node in a BM regime | Step 2: Calculating $\mathbf{A}$, $\vec{b}$, $\mathbf{C}$, $\vec{d}$, $\mathbf{E}$ and $f$ for tips and internal nodes | Step 3: Calculating $\mathbf{L}$, $\vec{m}$, $r$ for the internal nodes and the root | Step 4: Calculating the log-likelihood value using $\mathbf{L}{0}$, $\vec{m}{0}$, and $r_{0}$. | References

How to use the package? | Passing the function PCMInfoCpp as a metaI argument of PCMLik and/or PCMCreateLikelihood | Passing the meta-information object returned by PCMInfoCpp as a metaI argument of PCMLik and PCMCreateLikelihood