smfsb
package smfsb
Object containing basic types used throughout the library.
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Type Members
-
trait
CsvRow[T] extends AnyRef
Type class for vectors that can be rendered to a CSV string (and a Breeze DenseVector[Double]), extended by
State
-
implicit
class
CsvRowSyntax[T] extends AnyRef
Syntax for
CsvRow
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trait
DataSet[D] extends AnyRef
Data set type class, for ABC methods
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type
DoubleState = DenseVector[Double]
Alias for a Breeze
DenseVector[Double]
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type
HazardVec = DenseVector[Double]
Type for a SPN hazard vector
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type
IntState = DenseVector[Int]
Alias for a Breeze
DenseVector[Int]
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type
LogLik = Double
Type representing log-likelihoods - just an alias for
Double
.Type representing log-likelihoods - just an alias for
Double
. All likelihoods in this library are on a log scale. There should be no raw likelihoods passed into or out of any user-facing function. -
case class
MarkedSpn[S](species: List[String], m: S, pre: DenseMatrix[Int], post: DenseMatrix[Int], h: (S, Time) ⇒ HazardVec)(implicit evidence$2: State[S]) extends Spn[S] with Product with Serializable
Case class for SPNs that include an initial marking
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case class
PMatrix[T](x: Int, y: Int, r: Int, c: Int, data: ParVector[T]) extends Product with Serializable
Comonadic pointed 2d image/matrix type (parallel implementation), used by the spatial simulation functions.
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case class
PVector[T](cur: Int, vec: ParVector[T]) extends Product with Serializable
Comonadic pointed vector type (parallel implementation), used by the spatial simulation functions.
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sealed
trait
Spn[S] extends AnyRef
Main trait for stochastic Petri nets (SPNs)
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trait
State[S] extends CsvRow[S]
State type class, with implementations for Breeze
DenseVector
Ints
andDoubles
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trait
Thinnable[F[_]] extends AnyRef
A type class for things which can be "thinned", with an implementation for
Stream
.A type class for things which can be "thinned", with an implementation for
Stream
. Useful for MCMC algorithms. -
implicit
class
ThinnableSyntax[T, F[T]] extends AnyRef
Provision of the
.thin
syntax forThinnable
things -
type
Time = Double
Type representing time, but just an alias for
Double
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type
Ts[S] = List[(Time, S)]
The main time series class, for representing output from simulation functions, and for observed time course data
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case class
UnmarkedSpn[S](species: List[String], pre: DenseMatrix[Int], post: DenseMatrix[Int], h: (S, Time) ⇒ HazardVec)(implicit evidence$1: State[S]) extends Spn[S] with Product with Serializable
Case class for SPNs without an initial marking
Value Members
-
implicit
val
dvdState: State[DoubleState]
Evidence that
DoubleState
belongs to theState
type class -
implicit
val
dviState: State[IntState]
Evidence that
IntState
belongs to theState
type class -
implicit
val
streamThinnable: Thinnable[Stream]
A
Thinnable
instance forStream
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implicit
val
tsdsDs: DataSet[Ts[DoubleState]]
Evidence that
Ts[DoubleState]
is aDataSet
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implicit
val
tsisDs: DataSet[Ts[IntState]]
Evidence that
Ts[IntState]
is aDataSet
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object
Abc
Functions for parameter inference using ABC (and ABC-SMC) methods
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object
Mcmc
Functions for constucting generic Metropolis-Hastings MCMC algorithms, and associated utilities.
Functions for constucting generic Metropolis-Hastings MCMC algorithms, and associated utilities. Can be used in conjunction with an unbiased estimate of marginal model likelihood for constructing pseudo-marginal MCMC algorithms, such as PMMH pMCMC.
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object
Mll
Functions associated with particle filtering of Markov process models against time series data and the computation of marginal model likelihoods.
- object PMatrix extends Product with Serializable
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object
Sim
Functions for simulating data associated with a Markov process given an appropriate transition kernel.
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object
Spatial
All functions and utilities relating to spatial simulation
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object
SpnModels
Some example SPN models, each of which can be instantiated with either discrete or continous states.
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object
Step
Functions which accept a
Spn
and return a function for simulating from the transition kernel of that model