Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  def +(other: String): String

   Implicit

   This member is added by an implicit conversion from Operators to any2stringadd[Operators] performed by method any2stringadd in scala.Predef.

   Definition Classes

   any2stringadd

4.  def ->[B](y: B): (Operators, B)

   Implicit

   This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.

   Definition Classes

   ArrowAssoc

   Annotations

   @inline()

5.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

6.  implicit def array2VectorExpression(x: Array[Double]): VectorLift
7.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

8.  def beta(x: Double, y: Double): Double

   The beta function, also called the Euler integral of the first kind.

   The beta function, also called the Euler integral of the first kind.

   B(x, y) = ∫₀¹ t^x-1 (1-t)^y-1dt

   for x, y > 0 and the integration is over [0,1].The beta function is symmetric, i.e. B(x,y) = B(y,x).

9.  def chisqtest(table: Array[Array[Int]]): CorTest

   Given a two-dimensional contingency table in the form of an array of integers, returns Chi-square test for independence.

   Given a two-dimensional contingency table in the form of an array of integers, returns Chi-square test for independence. The rows of contingency table are labels by the values of one nominal variable, the columns are labels by the values of the other nominal variable, and whose entries are non-negative integers giving the number of observed events for each combination of row and column. Continuity correction will be applied when computing the test statistic for 2x2 tables: one half is subtracted from all |O-E| differences. The correlation coefficient is calculated as Cramer's V.

10.  def chisqtest(x: Array[Int], prob: Array[Double], constraints: Int = 1): ChiSqTest

    One-sample chisq test.

    One-sample chisq test. Given the array x containing the observed numbers of events, and an array prob containing the expected probabilities of events, and given the number of constraints (normally one), a small value of p-value indicates a significant difference between the distributions.

11.  def chisqtest2(x: Array[Int], y: Array[Int], constraints: Int = 1): ChiSqTest

    Two-sample chisq test.

    Two-sample chisq test. Given the arrays x and y, containing two sets of binned data, and given one constraint, a small value of p-value indicates a significant difference between two distributions.

12.  def cholesky(A: MatrixExpression): CholeskyDecomposition

    Cholesky decomposition.

13.  def cholesky(A: DenseMatrix): CholeskyDecomposition

    Cholesky decomposition.

14.  def cholesky(A: Array[Array[Double]]): CholeskyDecomposition

    Cholesky decomposition.

15.  def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

16.  def det(A: MatrixExpression): Double

    Returns the determinant of matrix.

17.  def det(A: DenseMatrix): Double

    Returns the determinant of matrix.

18.  def diag(A: Matrix): Array[Double]

    Returns the diagonal elements of matrix.

19.  def digamma(x: Double): Double

    The digamma function is defined as the logarithmic derivative of the gamma function.

20.  def eigen(A: DenseMatrix, k: Int): EigenValueDecomposition

    Eigen decomposition.

21.  def eigen(A: MatrixExpression): EigenValueDecomposition

    Eigen decomposition.

22.  def eigen(A: DenseMatrix): EigenValueDecomposition

    Eigen decomposition.

23.  def eigen(A: Array[Array[Double]]): EigenValueDecomposition

    Eigen decomposition.

24.  def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators

    Implicit

    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.

    Definition Classes

    Ensuring

25.  def ensuring(cond: (Operators) ⇒ Boolean): Operators

    Implicit

    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.

    Definition Classes

    Ensuring

26.  def ensuring(cond: Boolean, msg: ⇒ Any): Operators

    Implicit

    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.

    Definition Classes

    Ensuring

27.  def ensuring(cond: Boolean): Operators

    Implicit

    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.

    Definition Classes

    Ensuring

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

    Definition Classes

    AnyRef

29.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

30.  def erf(x: Double): Double

    The error function (also called the Gauss error function) is a special function of sigmoid shape which occurs in probability, statistics, materials science, and partial differential equations.

    The error function (also called the Gauss error function) is a special function of sigmoid shape which occurs in probability, statistics, materials science, and partial differential equations. It is defined as:

    erf(x) = ∫₀^x e^-t2dt

    The complementary error function, denoted erfc, is defined as erfc(x) = 1 - erf(x). The error function and complementary error function are special cases of the incomplete gamma function.

31.  def erfc(x: Double): Double

    The complementary error function.

32.  def erfcc(x: Double): Double

    The complementary error function with fractional error everywhere less than 1.2 × 10^-7.

    The complementary error function with fractional error everywhere less than 1.2 × 10^-7. This concise routine is faster than erfc.

33.  def eye(m: Int, n: Int): ColumnMajorMatrix

    Returns an m-by-n identity matrix.

34.  def eye(n: Int): ColumnMajorMatrix

    Returns an n-by-n identity matrix.

35.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

36.  def formatted(fmtstr: String): String

    Implicit

    This member is added by an implicit conversion from Operators to StringFormat[Operators] performed by method StringFormat in scala.Predef.

    Definition Classes

    StringFormat

    Annotations

    @inline()

37.  def ftest(x: Array[Double], y: Array[Double]): FTest

    Test if the arrays x and y have significantly different variances.

    Test if the arrays x and y have significantly different variances. Small values of p-value indicate that the two arrays have significantly different variances.

38.  def gamma(x: Double): Double

    Gamma function.

    Gamma function. Lanczos approximation (6 terms).

39.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

40.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

41.  def inv(A: MatrixExpression): DenseMatrix

    Returns the inverse of matrix.

42.  def inv(A: DenseMatrix): DenseMatrix

    Returns the inverse of matrix.

43.  def inverf(p: Double): Double

    The inverse error function.

44.  def inverfc(p: Double): Double

    The inverse complementary error function.

45.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

46.  def kendalltest(x: Array[Double], y: Array[Double]): CorTest

    Kendall rank correlation test.

    Kendall rank correlation test. The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant. It is used with non-parametric data. The p-value is calculated by approximation, which is good for n > 10.

47.  def kstest(x: Array[Double], y: Array[Double]): KSTest

    The two-sample KS test for the null hypothesis that the data sets are drawn from the same distribution.

    The two-sample KS test for the null hypothesis that the data sets are drawn from the same distribution. Small values of p-value show that the cumulative distribution function of x is significantly different from that of y. The arrays x and y are modified by being sorted into ascending order.

48.  def kstest(x: Array[Double], y: Distribution): KSTest

    The one-sample KS test for the null hypothesis that the data set x is drawn from the given distribution.

    The one-sample KS test for the null hypothesis that the data set x is drawn from the given distribution. Small values of p-value show that the cumulative distribution function of x is significantly different from the given distribution. The array x is modified by being sorted into ascending order.

49.  def lgamma(x: Double): Double

    log of the Gamma function.

    log of the Gamma function. Lanczos approximation (6 terms)

50.  def lu(A: MatrixExpression): LUDecomposition

    LU decomposition.

51.  def lu(A: DenseMatrix): LUDecomposition

    LU decomposition.

52.  def lu(A: Array[Array[Double]]): LUDecomposition

    LU decomposition.

53.  implicit def matrix2MatrixExpression(x: DenseMatrix): MatrixLift
54.  implicit def matrixExpression2Array(exp: MatrixExpression): DenseMatrix
55.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

56.  final def notify(): Unit

    Definition Classes

    AnyRef

57.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

58.  def ones(m: Int, n: Int): ColumnMajorMatrix

    Returns an m-by-n matrix of all ones.

59.  def ones(n: Int): ColumnMajorMatrix

    Returns an n-by-n matrix of all ones.

60.  def pearsontest(x: Array[Double], y: Array[Double]): CorTest

    Pearson correlation coefficient test.

61.  implicit def pimpArray2D(data: Array[Array[Double]]): PimpedArray2D
62.  implicit def pimpDouble(x: Double): PimpedDouble
63.  implicit def pimpDoubleArray(data: Array[Double]): PimpedDoubleArray
64.  implicit def pimpIntArray(data: Array[Int]): PimpedArray[Int]
65.  implicit def pimpMatrix(matrix: DenseMatrix): PimpedMatrix
66.  def qr(A: MatrixExpression): QRDecomposition

    QR decomposition.

67.  def qr(A: DenseMatrix): QRDecomposition

    QR decomposition.

68.  def qr(A: Array[Array[Double]]): QRDecomposition

    QR decomposition.

69.  def rank(A: MatrixExpression): Int

    Returns the rank of matrix.

70.  def rank(A: DenseMatrix): Int

    Returns the rank of matrix.

71.  def spearmantest(x: Array[Double], y: Array[Double]): CorTest

    Spearman rank correlation coefficient test.

    Spearman rank correlation coefficient test. The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie. when variables are ordinal). It can be used when there is non-parametric data and hence Pearson cannot be used.

    The raw scores are converted to ranks and the differences between the ranks of each observation on the two variables are calculated.

    The p-value is calculated by approximation, which is good for n > 10.

72.  def svd(A: DenseMatrix, k: Int): SingularValueDecomposition

    SVD decomposition.

73.  def svd(A: MatrixExpression): SingularValueDecomposition

    SVD decomposition.

74.  def svd(A: DenseMatrix): SingularValueDecomposition

    SVD decomposition.

75.  def svd(A: Array[Array[Double]]): SingularValueDecomposition

    SVD decomposition.

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

    Definition Classes

    AnyRef

77.  def toString(): String

    Definition Classes

    AnyRef → Any

78.  def trace(A: Matrix): Double

    Returns the trace of matrix.

79.  def ttest(x: Array[Double], y: Array[Double]): TTest

    Given the paired arrays x and y, test if they have significantly different means.

    Given the paired arrays x and y, test if they have significantly different means. Small values of p-value indicate that the two arrays have significantly different means.

80.  def ttest(x: Array[Double], mean: Double): TTest

    Independent one-sample t-test whether the mean of a normally distributed population has a value specified in a null hypothesis.

    Independent one-sample t-test whether the mean of a normally distributed population has a value specified in a null hypothesis. Small values of p-value indicate that the array has significantly different mean.

81.  def ttest2(x: Array[Double], y: Array[Double], equalVariance: Boolean = false): TTest

    Test if the arrays x and y have significantly different means.

    Test if the arrays x and y have significantly different means. Small values of p-value indicate that the two arrays have significantly different means.

    equalVariance

    true if the data arrays are assumed to be drawn from populations with the same true variance. Otherwise, The data arrays are allowed to be drawn from populations with unequal variances.

82.  implicit def vectorExpression2Array(exp: VectorExpression): Array[Double]
83.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

86.  def zeros(m: Int, n: Int): ColumnMajorMatrix

    Returns an m-by-n zero matrix.

87.  def zeros(n: Int): ColumnMajorMatrix

    Returns an n-by-n zero matrix.

88.  def →[B](y: B): (Operators, B)

    Implicit

    This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.

    Definition Classes

    ArrowAssoc