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smfsb

package smfsb

Object containing basic types used throughout the library.

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Type Members

  1. 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

  2. implicit class CsvRowSyntax[T] extends AnyRef

    Syntax for CsvRow

  3. trait DataSet[D] extends AnyRef

    Data set type class, for ABC methods

  4. type DoubleState = DenseVector[Double]

    Alias for a Breeze DenseVector[Double]

  5. type HazardVec = DenseVector[Double]

    Type for a SPN hazard vector

  6. type IntState = DenseVector[Int]

    Alias for a Breeze DenseVector[Int]

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. sealed trait Spn[S] extends AnyRef

    Main trait for stochastic Petri nets (SPNs)

  12. trait State[S] extends CsvRow[S]

    State type class, with implementations for Breeze DenseVector Ints and Doubles

  13. 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.

  14. implicit class ThinnableSyntax[T, F[T]] extends AnyRef

    Provision of the .thin syntax for Thinnable things

  15. type Time = Double

    Type representing time, but just an alias for Double

  16. type Ts[S] = List[(Time, S)]

    The main time series class, for representing output from simulation functions, and for observed time course data

  17. 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

  1. implicit val dvdState: State[DoubleState]

    Evidence that DoubleState belongs to the State type class

  2. implicit val dviState: State[IntState]

    Evidence that IntState belongs to the State type class

  3. implicit val streamThinnable: Thinnable[LazyList]

    A Thinnable instance for Stream

  4. implicit val tsdsDs: DataSet[Ts[DoubleState]]

    Evidence that Ts[DoubleState] is a DataSet

  5. implicit val tsisDs: DataSet[Ts[IntState]]

    Evidence that Ts[IntState] is a DataSet

  6. object Abc

    Functions for parameter inference using ABC (and ABC-SMC) methods

  7. 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.

  8. object Mll

    Functions associated with particle filtering of Markov process models against time series data and the computation of marginal model likelihoods.

  9. case object PMatrix extends Product with Serializable
  10. object Sim

    Functions for simulating data associated with a Markov process given an appropriate transition kernel.

  11. object Spatial

    All functions and utilities relating to spatial simulation

  12. object SpnModels

    Some example SPN models, each of which can be instantiated with either discrete or continous states.

  13. object Step

    Functions which accept a Spn and return a function for simulating from the transition kernel of that model

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