cheshire-likelihood
cheshire-likelihood
cheshire.likelihood
LikelihoodEvaluation
LikelihoodEvaluation
LikelihoodKernel
Partition
Partition
PartitionKernel
EdgeLikelihood
NodeLikelihood
PartitionKernel
TreeLikelihood
TreeLikelihood
PostOrderLeaf
PostOrderNode
PreOrderNode
PreOrderRootParent
cheshire-likelihood
/
cheshire.likelihood
/
PartitionKernel
PartitionKernel
trait
PartitionKernel
[F[_], R]
Companion
object
Graph
Supertypes
class
Object
trait
Matchable
class
Any
Type members
Value members
Type members
Classlikes
trait
EdgeLikelihood
trait
NodeLikelihood
Types
type
Clv
=
NodeClv
|
TipClv
type
Matrix
type
Model
type
NodeClv
type
Partial
=
Ppv
|
Clv
type
Ppv
type
TipClv
Value members
Abstract methods
def
allocateClv
:
Resource
[
F
,
NodeClv
]
def
allocateMatrix
:
Resource
[
F
,
Matrix
]
def
allocateModel
:
Resource
[
F
,
Model
]
def
allocatePpv
:
Resource
[
F
,
Ppv
]
def
backcast
(y:
Clv
, P:
Matrix
, x:
NodeClv
):
F
[
Unit
]
def
backcastProduct
(y:
Clv
, Py:
Matrix
, z:
Clv
, Pz:
Matrix
, x:
NodeClv
):
F
[
Unit
]
def
categoryCount
:
Int
def
computeMatrix
(model:
Model
, t:
R
, P:
Matrix
):
F
[
Unit
]
def
edgeLikelihood
:
Resource
[
F
,
EdgeLikelihood
]
def
forecast
(x:
Ppv
, P:
Matrix
, y:
Ppv
):
F
[
Unit
]
def
initModel
(freqs:
IndexedSeq
[
R
], params:
IndexedSeq
[
R
], rate:
R
, alpha:
R
, model:
Model
):
F
[
Unit
]
def
integrateProduct
(x:
Ppv
, y:
Clv
):
F
[
R
]
def
nodeLikelihood
:
Resource
[
F
,
NodeLikelihood
]
@
targetName
("ppvProduct")
def
product
(x:
Ppv
, y:
Clv
, z:
Ppv
):
F
[
Unit
]
@
targetName
("clvProduct")
def
product
(x:
Clv
, y:
Clv
, z:
NodeClv
):
F
[
Unit
]
def
rates
(model:
Model
):
F
[
IndexedSeq
[
R
]]
def
seed
(model:
Model
, x:
Ppv
):
F
[
Unit
]
def
seedAndIntegrate
(model:
Model
, x:
Clv
):
F
[
R
]
def
tips
:
IndexedSeq
[
TipClv
]