Partition

cheshire.likelihood.Partition
See thePartition companion object
trait Partition[F[_], R]

Attributes

Companion:
object
Source:
Partition.scala
Graph
Supertypes
class Object
trait Matchable
class Any

Members list

Concise view

Type members

Types

type Clv = NodeClv | TipClv

Attributes

Source:
Partition.scala
type Matrix

Attributes

Source:
Partition.scala
type Model

Attributes

Source:
Partition.scala
type NodeClv

Attributes

Source:
Partition.scala
type Partial = Ppv | Clv

Attributes

Source:
Partition.scala
type Ppv

Attributes

Source:
Partition.scala
type TipClv

Attributes

Source:
Partition.scala

Value members

Abstract methods

def backcast(y: Clv, P: Matrix): F[NodeClv]

Attributes

Source:
Partition.scala
def backcastProduct(y: Clv, Py: Matrix, z: Clv, Pz: Matrix): F[NodeClv]

Attributes

Source:
Partition.scala

Attributes

Source:
Partition.scala
def edgeLikelihood(model: Model, ppv: Ppv, clv: Clv)(t: R): F[LikelihoodEvaluation[F, R]]

Attributes

Source:
Partition.scala
def forecast(x: Ppv, P: Matrix): F[Ppv]

Attributes

Source:
Partition.scala
def integrateProduct(x: Ppv, y: Clv): F[R]

Attributes

Source:
Partition.scala
def matrix(model: Model, t: R): F[Matrix]

Attributes

Source:
Partition.scala
def model(freqs: IndexedSeq[R], params: IndexedSeq[R], rate: R, alpha: R): F[Model]

Attributes

Source:
Partition.scala
def nodeLikelihood(model: Model, ppv: Ppv, parentHeight: R, leftClv: Clv, leftHeight: R, rightClv: Clv, rightHeight: R)(t: R): F[LikelihoodEvaluation[F, R]]

Attributes

Source:
Partition.scala
def product(x: Ppv, y: Clv): F[Ppv]

Attributes

Source:
Partition.scala
def product(x: Clv, y: Clv): F[NodeClv]

Attributes

Source:
Partition.scala
def rates(model: Model): F[IndexedSeq[R]]

Attributes

Source:
Partition.scala
def seed(model: Model): F[Ppv]

Attributes

Source:
Partition.scala
def seedAndIntegrate(model: Model, x: Clv): F[R]

Attributes

Source:
Partition.scala

Attributes

Source:
Partition.scala