Packages

  • package root
    Definition Classes
    root
  • package scalation

    The scalation package specifies system-wide constants for directory paths.

    The scalation package specifies system-wide constants for directory paths. Sub-packages may wish to define 'BASE-DIR = DATA_DIR + ⁄ + <package>' in their own 'package.scala' files. For maintainability, directory paths should only be specified in 'package.scala' files.

    Definition Classes
    root
  • package activity

    The activity package contains classes, traits and objects for activity-oriented simulation models (for example, Petri Nets).

    The activity package contains classes, traits and objects for activity-oriented simulation models (for example, Petri Nets).

    Definition Classes
    scalation
  • package analytics

    The analytics package contains classes, traits and objects for analytics including clustering and prediction.

    The analytics package contains classes, traits and objects for analytics including clustering and prediction.

    Definition Classes
    scalation
  • package animation

    The animation package contains classes, traits and objects for 2D animation of simulation objects.

    The animation package contains classes, traits and objects for 2D animation of simulation objects.

    Definition Classes
    scalation
  • package calculus

    The calculus package contains classes with methods for computing derivatives, gradient vectors, Jacobian matrices, integrals and basic operators in Functional Analysis.

    The calculus package contains classes with methods for computing derivatives, gradient vectors, Jacobian matrices, integrals and basic operators in Functional Analysis.

    Definition Classes
    scalation
  • package dynamics

    The dynamics package contains classes, traits and objects for system dynamics simulations using Ordinary Differential Equations (ODEs).

    The dynamics package contains classes, traits and objects for system dynamics simulations using Ordinary Differential Equations (ODEs).

    Definition Classes
    scalation
  • package dynamics_pde

    The dynamics_pde package contains classes, traits and objects for system dynamics simulations using Partial Differential Equations 'PDE's.

    The dynamics_pde package contains classes, traits and objects for system dynamics simulations using Partial Differential Equations 'PDE's.

    Definition Classes
    scalation
  • package event

    The event package contains classes, traits and objects for event oriented simulation models (for example, event scheduling or event graphs).

    The event package contains classes, traits and objects for event oriented simulation models (for example, event scheduling or event graphs).

    Definition Classes
    scalation
  • package graphalytics

    The graphalytics package contains classes, traits and objects for graph analytics on Trees, 'DAG's and Directed Graphs.

    The graphalytics package contains classes, traits and objects for graph analytics on Trees, 'DAG's and Directed Graphs. It provides an implicit conversion when needed for converting doubles to vectors.

    Definition Classes
    scalation
  • package linalgebra

    The linalgebra package contains classes, traits and objects for linear algebra, including vectors and matrices for real and complex numbers.

    The linalgebra package contains classes, traits and objects for linear algebra, including vectors and matrices for real and complex numbers.

    Definition Classes
    scalation
  • package math

    The math package contains classes, traits and objects for common mathematical operations.

    The math package contains classes, traits and objects for common mathematical operations. Its package object defines exponentiation, logarithmic, trigonometric, etc. operators and functions.

    Definition Classes
    scalation
  • package maxima

    The maxima package contains classes, traits and objects for optimization to find maxima.

    The maxima package contains classes, traits and objects for optimization to find maxima.

    Definition Classes
    scalation
  • package minima

    The minima package contains classes, traits and objects for optimization to find minima.

    The minima package contains classes, traits and objects for optimization to find minima.

    Definition Classes
    scalation
  • package model

    The model package contains a trait giving a template for models to follow.

    The model package contains a trait giving a template for models to follow.

    Definition Classes
    scalation
  • package moleculesampleapp
    Definition Classes
    scalation
  • package plot

    The plot package contains classes, traits and objects for simple plotting of x-y data.

    The plot package contains classes, traits and objects for simple plotting of x-y data.

    Definition Classes
    scalation
  • package process

    The process package contains classes, traits and objects for process-oriented simulation models (for example, process-interaction).

    The process package contains classes, traits and objects for process-oriented simulation models (for example, process-interaction).

    Definition Classes
    scalation
  • package queueingnet

    The queueingnet package contains classes, traits and objects for solving simple queueing network problems.

    The queueingnet package contains classes, traits and objects for solving simple queueing network problems.

    Definition Classes
    scalation
  • package random

    The random package contains classes, traits and objects for the generation of random numbers.

    The random package contains classes, traits and objects for the generation of random numbers.

    Definition Classes
    scalation
  • package relalgebra

    The relalgebra package contains classes, traits and objects for columnar relational algebra, where columns are vectors from the linalgebra package.

    The relalgebra package contains classes, traits and objects for columnar relational algebra, where columns are vectors from the linalgebra package.

    Definition Classes
    scalation
  • package scala2d

    The scala2d package contains classes, traits and objects for for simple 2D graphics in scala, based upon java.swing, java.awt and java_awt_geom.

    The scala2d package contains classes, traits and objects for for simple 2D graphics in scala, based upon java.swing, java.awt and java_awt_geom.

    Definition Classes
    scalation
  • package scala3d

    The scala3d package contains classes, traits and objects for for simple 3D graphics in scala, based upon javafx.

    The scala3d package contains classes, traits and objects for for simple 3D graphics in scala, based upon javafx. Its package object contains type aliases for 'javafx.geometry.Point3D' and all subclasses of 'javafx.scene.shape.Shape3D'.

    Definition Classes
    scalation
  • package stat

    The stat package contains classes, traits and objects for basic statistical functions and analyses.

    The stat package contains classes, traits and objects for basic statistical functions and analyses. The package object itself defines an implicit conversion from scalation.linalgebra.VectorD to StatVector.

    Definition Classes
    scalation
  • Anova
  • AnovaTest
  • BatchVector
  • BatchVectorTest
  • Canvas
  • Conversions
  • FramelessHistogram
  • GoodnessOfFit_CS
  • GoodnessOfFit_CS2
  • GoodnessOfFit_CS2Test
  • GoodnessOfFit_CSTest
  • GoodnessOfFit_KS
  • GoodnessOfFit_KSTest
  • GoodnessOfFit_KSTest2
  • GoodnessOfFit_KSTest3
  • GoodnessOfFit_KSTest4
  • Histogram
  • HistogramTest
  • MM_StatVector
  • MM_StatVectorTest
  • MM_StatVectorTest2
  • MethodOfMoments
  • Normality
  • NormalityTest
  • Q_Q_Plot
  • Q_Q_PlotTest
  • Q_Q_PlotTest2
  • StatTable
  • StatTableTest
  • StatVector
  • StatVectorTest
  • StatVectorTest2
  • StatVectorTest3
  • StatVectorTest4
  • Statistic
  • StatisticTest
  • TimeStatistic
  • package state

    The state package contains classes, traits and objects for state-oriented simulation models (for example, Markov Chains).

    The state package contains classes, traits and objects for state-oriented simulation models (for example, Markov Chains).

    Definition Classes
    scalation
  • package tableau

    The tableau package contains classes for tableau oriented simulation models (similar to spreadsheet simulations).

    The tableau package contains classes for tableau oriented simulation models (similar to spreadsheet simulations).

    Definition Classes
    scalation
  • package util

    The util package contains classes, traits and objects for basic utility functions.

    The util package contains classes, traits and objects for basic utility functions.

    Definition Classes
    scalation

package stat

The stat package contains classes, traits and objects for basic statistical functions and analyses. The package object itself defines an implicit conversion from scalation.linalgebra.VectorD to StatVector.

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Type Members

  1. class Anova extends AnyRef

    The Anova class provides the functions necessary for performing a one-way ANalysis Of VAriance (ANOVA) on the input data matrix x.

    The Anova class provides the functions necessary for performing a one-way ANalysis Of VAriance (ANOVA) on the input data matrix x. The data matrix: row i - i-th group/treatment column j - j-th replica

  2. class BatchVector extends AnyRef

    The BatchVector class contains a VectorD for maintaining experimental data in multiple batches and methods for computing statistics on these data.

    The BatchVector class contains a VectorD for maintaining experimental data in multiple batches and methods for computing statistics on these data. Ex: It can be used to support the Method of Batch Means (MBM).

  3. class Canvas extends Panel

    Create a canvas on which to draw the histogram.

  4. class FramelessHistogram extends AnyRef

    The FramelessHistogram class should be used in embedded applications.

  5. class GoodnessOfFit_CS extends Error

    The GoodnessOfFit_CS class is used to fit data to probability distributions.

    The GoodnessOfFit_CS class is used to fit data to probability distributions. Choosing the number of 'intervals' can be challenging and can affect the result: Suggestions: each interval should have 'E_i = n*p_i >= 5' and intervals >= sqrt (n). It uses the Chi-square goodness of fit test with equal width intervals.

    See also

    www.eg.bucknell.edu/~xmeng/Course/CS6337/Note/master/node66.html Compute the following for each interval and sum over all intervals. (O_i - E_i)^2 / E_i where O_i and E_i are the observed and expected counts for interval 'i', respectively.

  6. class GoodnessOfFit_CS2 extends Error

    The GoodnessOfFit_CS2 class is used to fit data to probability distributions.

    The GoodnessOfFit_CS2 class is used to fit data to probability distributions. Suggestions: each interval should have 'E_i = n*p_i >= 5' and intervals >= sqrt (n). It uses the Chi-square goodness of fit test with equal probability intervals.

    See also

    www.eg.bucknell.edu/~xmeng/Course/CS6337/Note/master/node66.html Compute the following for each interval and sum over all intervals. (O_i - E_i)^2 / E_i where O_i and E_i are the observed and expected counts for interval 'i', respectively.

  7. class GoodnessOfFit_KS extends Error

    The GoodnessOfFit_KS class is used to fit data to a probability distribution.

    The GoodnessOfFit_KS class is used to fit data to a probability distribution. It uses the Kolmogorov-Smirnov Goodness of Fit Test. Determine the maximum absolute difference 'd_max' between the data points 'd_i' and the theoretical value from a CDF F,

    d_max = max 1≤i≤n {|Fe(d_i) - F(d_i)|} (concept)

    d_max = max 1≤i≤n {F(d_i) − (i−1)/n, i/n − F(d_i)} (calculation)

    where 'Fe(.)' is the empirical distribution and 'F(.)' is the theoretical distribution. If 'd_max' is large, the fit should be rejected.

    See also

    www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm ---------------------------------------------------------------------------------

    www.eg.bucknell.edu/~xmeng/Course/CS6337/Note/master/node66.html

  8. class Histogram extends VizFrame

    The Histogram class takes a vector of values, counts the number of values in each of several intervals and displays the counts vertically in a histogram.

  9. final class MM_StatVector extends AnyVal

    The MM_StatVector value class provides methods for computing statistics on data vectors.

    The MM_StatVector value class provides methods for computing statistics on data vectors. Both maximum likelihood and unbiased estimators are supported. Unbiased should only be used on sample (not population) data. Ex: It can be used to support the Method of Independent Replications (MIR). For efficiency, MM_StatVector is a value class that enriches the VectorD. The corresponding implicit conversion in the stat package object.

    See also

    stackoverflow.com/questions/14861862/how-do-you-enrich-value-classes-without-overhead -----------------------------------------------------------------------------

  10. class StatTable extends Frame

    The StatTable class display statistical results in a frame's table.

  11. final class StatVector extends AnyVal

    The StatVector value class provides methods for computing statistics on data vectors.

    The StatVector value class provides methods for computing statistics on data vectors. Both maximum likelihood and unbiased estimators are supported. Unbiased should only be used on sample (not population) data. Ex: It can be used to support the Method of Independent Replications (MIR). For efficiency, StatVector is a value class that enriches the VectorD. The corresponding implicit conversion in the stat package object.

    See also

    stackoverflow.com/questions/14861862/how-do-you-enrich-value-classes-without-overhead -----------------------------------------------------------------------------

  12. class Statistic extends Error

    The Statistic class is used to collect values and compute sample statistics on them (e.g., waiting time).

    The Statistic class is used to collect values and compute sample statistics on them (e.g., waiting time). Contrast with TimeStatistic defined below.

  13. class TimeStatistic extends Statistic

    The TimeStatistic class is used to collect values and compute time-persistent statistics on them (e.g., Number in Queue).

    The TimeStatistic class is used to collect values and compute time-persistent statistics on them (e.g., Number in Queue).

    See also

    staff.unak.is/andy/Year%203%20Simulation/Laboratories/v4manual/internal.htm

Value Members

  1. implicit def vectorC2StatVector(x: VectorC): StatVector

    Implicit conversion from VectorD to StatVector, which supports more advanced statistical operations on vectors (e.g., covariance).

    Implicit conversion from VectorD to StatVector, which supports more advanced statistical operations on vectors (e.g., covariance). Other vector types require to conversion to VectorD via 'toDouble'. Caveat: won't work for vectors of string numbers (VectorS).

    x

    the vector to be enriched

  2. implicit def vectorD2StatVector(x: VectorD): StatVector
  3. implicit def vectorI2StatVector(x: VectorI): StatVector
  4. implicit def vectorL2StatVector(x: VectorL): StatVector
  5. implicit def vectorQ2StatVector(x: VectorQ): StatVector
  6. implicit def vectorR2StatVector(x: VectorR): StatVector
  7. object AnovaTest extends App

    This object is used to test the Anova class.

    This object is used to test the Anova class.

    See also

    wiki.uiowa.edu/display/bstat/One-way+fixed+effects+ANOVA > run-main scalation.stat.AnovaTest

  8. object BatchVectorTest extends App

    The BatchVectorTest object provides an example of how to use the BatchVector class to implement the Method of Batch Means (MBM).

  9. object Conversions

    The Conversions object provide implicit conversion from memory mapped vectors to StatVectors.

  10. object GoodnessOfFit_CS

    The GoodnessOfFit_CS companion object provides two discrepancy metrics to capture the differences between observed 'o' and expected 'o' counts.

    The GoodnessOfFit_CS companion object provides two discrepancy metrics to capture the differences between observed 'o' and expected 'o' counts.

    See also

    en.wikipedia.org/wiki/G-test

  11. object GoodnessOfFit_CS2Test extends App

    The GoodnessOfFit_CS2Test object is used to test the GoodnessOfFit_CS class.

    The GoodnessOfFit_CS2Test object is used to test the GoodnessOfFit_CS class.

    See also

    www.seattlecentral.edu/qelp/sets/057/057.html > run-main scalation.stat.GoodnessOfFit_CS2Test

  12. object GoodnessOfFit_CSTest extends App

    The GoodnessOfFit_CSTest object is used to test the GoodnessOfFit_CS class.

    The GoodnessOfFit_CSTest object is used to test the GoodnessOfFit_CS class.

    See also

    www.seattlecentral.edu/qelp/sets/057/057.html > run-main scalation.stat.GoodnessOfFit_CSTest

  13. object GoodnessOfFit_KS

    The GoodnessOfFit_KS object provides methods to approximate the critical values/p-values for the KS Test.

    The GoodnessOfFit_KS object provides methods to approximate the critical values/p-values for the KS Test.

    P(D_n < d)

    See also

    sa-ijas.stat.unipd.it/sites/sa-ijas.stat.unipd.it/files/IJAS_3-4_2009_07_Facchinetti.pdf

    www.jstatsoft.org/article/view/v008i18/kolmo.pdf

  14. object GoodnessOfFit_KSTest extends App

    The GoodnessOfFit_KSTest object is used to test the GoodnessOfFit_KS class.

    The GoodnessOfFit_KSTest object is used to test the GoodnessOfFit_KS class.

    See also

    www.seattlecentral.edu/qelp/sets/057/057.html > run-main scalation.stat.GoodnessOfFit_KSTest

  15. object GoodnessOfFit_KSTest2 extends App

    The GoodnessOfFit_KSTest2 object is used to test the GoodnessOfFit_KS object.

    The GoodnessOfFit_KSTest2 object is used to test the GoodnessOfFit_KS object.

    See also

    www.utdallas.edu/~herve/Abdi-Lillie2007-pretty.pdf > run-main scalation.stat.GoodnessOfFit_KSTest2

  16. object GoodnessOfFit_KSTest3 extends App

    The GoodnessOfFit_KSTest3 object is used to test the GoodnessOfFit_KS object.

    The GoodnessOfFit_KSTest3 object is used to test the GoodnessOfFit_KS object.

    See also

    www.jstatsoft.org/article/view/v008i18/kolmo.pdf > run-main scalation.stat.GoodnessOfFit_KSTest3

  17. object GoodnessOfFit_KSTest4 extends App

    The GoodnessOfFit_KSTest4 object is used to test the GoodnessOfFit_KS object.

    The GoodnessOfFit_KSTest4 object is used to test the GoodnessOfFit_KS object. Fifty data points generated by a random variate generator for the Normal distribution are tested to see if they pass the Normality test. > run-main scalation.stat.GoodnessOfFit_KSTest4

  18. object HistogramTest extends App

    The HistogramTest object is used to test the Histogram class.

    The HistogramTest object is used to test the Histogram class. As 'k' increases, the sum of Uniform approaches Normal. > run-main scalation.stat.HistogramTest

  19. object MM_StatVectorTest extends App

    The MM_StatVectorTest object is used to test the MM_StatVector class.

    The MM_StatVectorTest object is used to test the MM_StatVector class. > run-main scalation.stat.MM_StatVectorTest

  20. object MM_StatVectorTest2 extends App

    The MM_StatVectorTest2 object provides an example of how to use the MM_StatVector class to implement the Method of Independent Replications (MIR) following a simple two-stage procedure.

  21. object MethodOfMoments

    The MethodOfMoments object provides methods for estimating parameters for popular probability distributions using the Method of Moments (MOM).

    The MethodOfMoments object provides methods for estimating parameters for popular probability distributions using the Method of Moments (MOM). The main alternative is to use Maximum Likelihood Estimators (MLE).

    See also

    www.math.uah.edu/stat/point/Moments.html

  22. object Normality

    The Normality object provides methods for testing Normality based on skewness and kurtosis.

    The Normality object provides methods for testing Normality based on skewness and kurtosis. Such test are more suitable for large sample sizes where more powerful goodness-of-fit tests tend to frequently reject Normality.

    See also

    en.wikipedia.org/wiki/D%27Agostino%27s_K-squared_test

    stats.stackexchange.com/questions/2492/is-normality-testing-essentially-useless

    stats.stackexchange.com/questions/29731/regression-when-the-ols-residuals-are-not-normally-distributed

  23. object NormalityTest extends App

    The NormalityTest object is used to test the Normality object.

    The NormalityTest object is used to test the Normality object.

    See also

    www.seattlecentral.edu/qelp/sets/057/057.html > run-main scalation.stat.NormalityTest

  24. object Q_Q_Plot extends Error

    The Q_Q_Plot object produces Quantile-Quantile plots that are used to compare probability distributions.

  25. object Q_Q_PlotTest extends App

    The Q_Q_PlotTest object is used to test the Q_Q_Plot object: distribution vs.

    The Q_Q_PlotTest object is used to test the Q_Q_Plot object: distribution vs. distribution. > run-main scalation.stat.Q_Q_PlotTest

  26. object Q_Q_PlotTest2 extends App

    The Q_Q_PlotTest2 object is used to test the Q_Q_Plot object: data vector vs.

    The Q_Q_PlotTest2 object is used to test the Q_Q_Plot object: data vector vs. distribution. > run-main scalation.stat.Q_Q_PlotTest2

  27. object StatTableTest extends App

    The StatTableTest is used to test the StatTable class.

  28. object StatVector

    The StatVector companion object extends statistics vector operations to matrices.

  29. object StatVectorTest extends App

    The StatVectorTest object is used to test the StatVector class.

    The StatVectorTest object is used to test the StatVector class.

    See also

    www.mathworks.com/help/stats/skewness.html > run-main scalation.stat.StatVectorTest

  30. object StatVectorTest2 extends App

    The StatVectorTest2 object provides an example of how to use the StatVector class to implement the Method of Independent Replications (MIR) following a simple two-stage procedure.

    The StatVectorTest2 object provides an example of how to use the StatVector class to implement the Method of Independent Replications (MIR) following a simple two-stage procedure. > run-main scalation.stat.StatVectorTest2

  31. object StatVectorTest3 extends App

    The StatVectorTest3 object is used to test the StatVector companion object.

    The StatVectorTest3 object is used to test the StatVector companion object.

    See also

    www.itl.nist.gov/div898/handbook/pmc/section5/pmc541.htm > run-main scalation.stat.StatVectorTest3

  32. object StatVectorTest4 extends App

    The StatVectorTest4 object is used to test the StatVector companion object.

    The StatVectorTest4 object is used to test the StatVector companion object.

    See also

    www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/corrmatr.htm > run-main scalation.stat.StatVectorTest4

  33. object Statistic extends Error

    The Statistic companion object provides additional values and functions.

  34. object StatisticTest extends App

    The StatisticTest object is used to test the Statistic and TimeStatistic classes.

  35. object TimeStatistic extends Error

    The TimeStatistic companion object provides additional functions.

Inherited from AnyRef

Inherited from Any

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