cheshire-likelihood
0.0-3204382
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
Source:
PartitionKernel.scala
Graph
Supertypes
Self type
class
Object
trait
Matchable
class
Any
PartitionKernel
[
F
,
R
]
Type members
Value members
Type members
Classlikes
trait
EdgeLikelihood
Source:
PartitionKernel.scala
trait
NodeLikelihood
Source:
PartitionKernel.scala
Types
type
Clv
=
NodeClv
|
TipClv
Source:
PartitionKernel.scala
type
Matrix
Source:
PartitionKernel.scala
type
Model
Source:
PartitionKernel.scala
type
NodeClv
Source:
PartitionKernel.scala
type
Partial
=
Ppv
|
Clv
Source:
PartitionKernel.scala
type
Ppv
Source:
PartitionKernel.scala
type
TipClv
Source:
PartitionKernel.scala
Value members
Abstract methods
def
allocateClv
:
Resource
[
F
,
NodeClv
]
Source:
PartitionKernel.scala
def
allocateMatrix
:
Resource
[
F
,
Matrix
]
Source:
PartitionKernel.scala
def
allocateModel
:
Resource
[
F
,
Model
]
Source:
PartitionKernel.scala
def
allocatePpv
:
Resource
[
F
,
Ppv
]
Source:
PartitionKernel.scala
def
backcast
(
y:
Clv
,
P:
Matrix
,
x:
NodeClv
):
F
[
Unit
]
Source:
PartitionKernel.scala
def
backcastProduct
(
y:
Clv
,
Py:
Matrix
,
z:
Clv
,
Pz:
Matrix
,
x:
NodeClv
):
F
[
Unit
]
Source:
PartitionKernel.scala
def
categoryCount
:
Int
Source:
PartitionKernel.scala
def
computeMatrix
(
model:
Model
,
t:
R
,
P:
Matrix
):
F
[
Unit
]
Source:
PartitionKernel.scala
def
edgeLikelihood
:
Resource
[
F
,
EdgeLikelihood
]
Source:
PartitionKernel.scala
def
forecast
(
x:
Ppv
,
P:
Matrix
,
y:
Ppv
):
F
[
Unit
]
Source:
PartitionKernel.scala
def
initModel
(
freqs:
IndexedSeq
[
R
],
params:
IndexedSeq
[
R
],
rate:
R
,
alpha:
R
,
model:
Model
):
F
[
Unit
]
Source:
PartitionKernel.scala
def
integrateProduct
(
x:
Ppv
,
y:
Clv
):
F
[
R
]
Source:
PartitionKernel.scala
def
nodeLikelihood
:
Resource
[
F
,
NodeLikelihood
]
Source:
PartitionKernel.scala
def
product
(
x:
Ppv
,
y:
Clv
,
z:
Ppv
):
F
[
Unit
]
Source:
PartitionKernel.scala
def
product
(
x:
Clv
,
y:
Clv
,
z:
NodeClv
):
F
[
Unit
]
Source:
PartitionKernel.scala
def
rates
(
model:
Model
):
F
[
IndexedSeq
[
R
]]
Source:
PartitionKernel.scala
def
seed
(
model:
Model
,
x:
Ppv
):
F
[
Unit
]
Source:
PartitionKernel.scala
def
seedAndIntegrate
(
model:
Model
,
x:
Clv
):
F
[
R
]
Source:
PartitionKernel.scala
def
tips
:
IndexedSeq
[
TipClv
]
Source:
PartitionKernel.scala
Concrete methods
final
def
imap
[
S
](
f:
R
=>
S
)(
g:
S
=>
R
)(
using
Functor
[
F
]):
Aux
[
F
,
S
,
Model
,
Matrix
,
Ppv
,
NodeClv
,
TipClv
]
Source:
PartitionKernel.scala