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1.  final def !=(arg0: Any): Boolean

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2.  final def ##: Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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5.  def cle(n: Spn[DoubleState], dt: Double = 0.01): (DoubleState, Time, Time) => DoubleState

   An Euler-Maruyama simulation of a CLE approximation to the provided Spn.

   An Euler-Maruyama simulation of a CLE approximation to the provided Spn.

   n

   A Spn[DoubleState] model (note that the state must be continous)

   dt

   The internal time step of the algorithm. Not the same as the deltat of the returned transition kernel.

   returns

   A function with type signature (x0: DoubleState, t0: Time, deltat: Time) => DoubleState which will simulate the state of the system at time t0+deltat given initial state x0 and intial time t0

6.  def clone(): AnyRef

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   @throws(classOf[java.lang.CloneNotSupportedException]) @native()

7.  final def eq(arg0: AnyRef): Boolean

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8.  def equals(arg0: AnyRef): Boolean

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9.  def euler(n: Spn[DoubleState], dt: Double = 0.01): (DoubleState, Time, Time) => DoubleState

   A simple Euler integration of the continuous deterministic approximation to the provided Spn.

   A simple Euler integration of the continuous deterministic approximation to the provided Spn. Euler methods are well-known to be very unstable, but the function can be useful for getting a basic idea of how the model behaves in the absence of noise.

   n

   A Spn[DoubleState] model (note that the state must be continous)

   dt

   The internal time step of the algorithm. Not the same as the deltat of the returned transition kernel.

   returns

   A function with type signature (x0: DoubleState, t0: Time, deltat: Time) => DoubleState which will simulate the state of the system at time t0+deltat given initial state x0 and intial time t0

10.  def finalize(): Unit

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11.  final def getClass(): Class[_ <: AnyRef]

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    @native()

12.  def gillespie(n: Spn[IntState], minH: Double = 1e-20, maxH: Double = 1e6): (IntState, Time, Time) => IntState

    The Gillespie algorithm, sometimes known as the direct method, or the stochastic simulation algorithm (SSA)

    The Gillespie algorithm, sometimes known as the direct method, or the stochastic simulation algorithm (SSA)

    n

    A Spn[IntState] model

    minH

    Threshold for treating hazard as zero

    maxH

    Threshold for terminating simulation early

    returns

    A function with type signature (x0: IntState, t0: Time, deltat: Time) => IntState which will simulate the state of the system at time t0+deltat given initial state x0 and intial time t0

13.  def hashCode(): Int

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14.  final def isInstanceOf[T0]: Boolean

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15.  final def ne(arg0: AnyRef): Boolean

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16.  final def notify(): Unit

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    @native()

17.  final def notifyAll(): Unit

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    @native()

18.  def pts(n: Spn[IntState], dt: Double = 0.01): (IntState, Time, Time) => IntState

    A Poisson time-stepping algorithm.

    A Poisson time-stepping algorithm. Like a tau-leaping algorithm, but with fixed step sizes.

    n

    A Spn[IntState] model

    dt

    The internal time step of the algorithm. Not the same as the deltat of the returned transition kernel.

    returns

    A function with type signature (x0: IntState, t0: Time, deltat: Time) => IntState which will simulate the state of the system at time t0+deltat given initial state x0 and intial time t0

19.  final def synchronized[T0](arg0: => T0): T0

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20.  def toString(): String

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21.  final def wait(): Unit

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    @throws(classOf[java.lang.InterruptedException])

22.  final def wait(arg0: Long, arg1: Int): Unit

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    @throws(classOf[java.lang.InterruptedException])

23.  final def wait(arg0: Long): Unit

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    @throws(classOf[java.lang.InterruptedException]) @native()