ELynx.Simulate.MarkovProcessAlongTree

Calculate transition probability matrices, map rate matrices on trees, populate a tree with states according to a stationary distribution, etc.

The implementation of the Markov process is more than basic and can be improved in a lot of ways.

Single rate matrix.

simulate :: PrimMonad m => Int -> StationaryDistribution -> ExchangeabilityMatrix -> Tree Double -> Gen (PrimState m) -> m (Tree [State])

Simulate a number of sites for a given substitution model. Keep states at internal nodes. The result is a tree with the list of simulated states as node labels.

simulateAndFlatten :: PrimMonad m => Int -> StationaryDistribution -> ExchangeabilityMatrix -> Tree Double -> Gen (PrimState m) -> m [[State]]

Simulate a number of sites for a given substitution model. Only the states at the leafs are retained. The states at internal nodes are removed. This has a lower memory footprint.

XXX: Improve performance. Use vectors, not lists.

Mixture models.

simulateMixtureModel :: PrimMonad m => Int -> Vector R -> [StationaryDistribution] -> [ExchangeabilityMatrix] -> Tree Double -> Gen (PrimState m) -> m (Tree [State])

Simulate a number of sites for a given set of substitution models with corresponding weights. Keep states at internal nodes. See also simulate.

simulateAndFlattenMixtureModel :: PrimMonad m => Int -> Vector R -> [StationaryDistribution] -> [ExchangeabilityMatrix] -> Tree Double -> Gen (PrimState m) -> m [[State]]

Simulate a number of sites for a given set of substitution models with corresponding weights. Forget states at internal nodes. See also simulateAndFlatten.

simulateAndFlattenMixtureModelPar :: Int -> Vector R -> [StationaryDistribution] -> [ExchangeabilityMatrix] -> Tree Double -> GenIO -> IO [[[State]]]

See simulateAndFlattenMixtureModel, parallel version.