Value Members

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 beta(mod: Dlm): Dglm

   Construct a DGLM with Beta distributed observations, with variance < mean (1 - mean)

6.  def beta(mean: Double, variance: Double): ContinuousDistr[Double]

   A beta distribution parameterised by the mean and variance

   A beta distribution parameterised by the mean and variance

   mean

   the mean of the resulting beta distribution

   variance

   the variance of the beta distribution

   returns

   a beta distribution

7.  def clone(): AnyRef

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   @native() @throws( ... )

8.  def diagonal(m: DenseMatrix[Double]): Vector[Double]
9.  final def eq(arg0: AnyRef): Boolean

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10.  def equals(arg0: Any): Boolean

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11.  def expit(x: Double): Double
12.  def finalize(): Unit

    Attributes

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

13.  def forecastParticles(mod: Dglm, xt: Vector[DenseVector[Double]], p: DlmParameters, ys: Vector[Data]): Vector[(Double, Vector[DenseVector[Double]], Vector[DenseVector[Double]])]

    Forecast a DGLM from a particle cloud representing the latent state at the end of the observations

    Forecast a DGLM from a particle cloud representing the latent state at the end of the observations

    mod

    the model

    xt

    the particle cloud representing the latent state

    p

    the parameters of the model

    returns

    the time, mean observation and variance of the observation

14.  final def getClass(): Class[_]

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    @native()

15.  def hashCode(): Int

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16.  def initialiseState(model: Dglm, params: DlmParameters): (Data, DenseVector[Double])
17.  final def isInstanceOf[T0]: Boolean

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18.  def logisticFunction(upper: Double)(number: Double): Double

    Logistic function to transform the number onto a range between 0 and upper

    Logistic function to transform the number onto a range between 0 and upper

    upper

    the upper limit of the logistic function

    number

    the number to be transformed

    returns

    a number between 0 and upper

19.  def logit(p: Double): Double
20.  def meanAndIntervals(prob: Double)(samples: Seq[DenseVector[Double]]): (DenseVector[Double], Array[Double], Array[Double])
21.  def meanCovSamples(samples: Seq[DenseVector[Double]]): (DenseVector[Double], DenseMatrix[Double])

    Calculate the mean and covariance of a sequence of DenseVectors

22.  def medianAndIntervals(prob: Double)(samples: Seq[DenseVector[Double]]): (Array[Double], Array[Double], Array[Double])
23.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

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24.  def negativeBinomial(mod: Dlm): Dglm

    Negative Binomial Model for overdispersed count data

25.  final def notify(): Unit

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    @native()

26.  final def notifyAll(): Unit

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    @native()

27.  def observation(model: Dglm, params: DlmParameters, state: DenseVector[Double], time: Double): Rand[DenseVector[Double]]
28.  def poisson(mod: Dlm): Dglm

    Construct a DGLM with Poisson distributed observations

29.  def simStep(model: Dglm, params: DlmParameters)(state: DenseVector[Double], time: Double, dt: Double): Rand[(Data, DenseVector[Double])]
30.  def simulateRegular(model: Dglm, params: DlmParameters, dt: Double): Process[(Data, DenseVector[Double])]

    Simulate from a model using regular steps

31.  def stepOu(model: Dglm, params: DlmParameters)(state: DenseVector[Double], dt: Double): Rand[DenseVector[Double]]

    Advance the a multivariate state independently according to the ornstein uhlenbeck process

32.  def stepState(model: Dglm, params: DlmParameters)(state: DenseVector[Double], dt: Double): Rand[DenseVector[Double]]
33.  def studentT(nu: Int, mod: Dlm): Dglm

    Define a DGLM with Student's t observation errors

34.  final def synchronized[T0](arg0: ⇒ T0): T0

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35.  def toString(): String

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36.  final def wait(): Unit

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    @throws( ... )

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

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    @throws( ... )

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

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39.  def zip(mod: Dlm): Dglm

    Zero inflated Poisson DGLM, the observation variance is the logit of the probability of observing a zero

    Zero inflated Poisson DGLM, the observation variance is the logit of the probability of observing a zero

    mod

    the DLM model specifying the latent-state