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Sum( data[i] ) / data.length .
auto(data, 1, mean, variance).
z = slope*x + intercept by minimizing the sum of least squares
between z and the y.
x under the beta density
function.
x to
infinity) of the beta density function.
0 through k of the Binomial
probability density.
k+1 through n of the Binomial
probability density.
chi2, or the
"omnibus" test statistic, and the significance, or "p-value" of this test statistic.
x)
of the Chi square probability density function with
v degrees of freedom.
x to
infinity) of the Chi square probability density function
with v degrees of freedom.
a and b into a new array
c such that c = {a,b}.
size with
each element equal to the specified constValue.
size with
each element equal to the specified constValue.
y for a sequence of λ
and returns the array of linear correlation coefficients
between the x and the box-cox transformed y.
data1 and data2,
each of length N.
value.
ScatterPlot.drawScatterPlot(float[], float[], float, int).
Correlation.auto(float[], int, float, float))
in the residuals (prediction errors) from a regression analysis.
erfc.
x under the F density
function (also known as Snedcor's density or the
variance ratio density); formerly named fdtr.
fdtrc.
x of the F density is equal to p; formerly named fdtri.
Γ(x)
x of the gamma probability
density function.
x to infinity of the gamma
probability density function:
L.
data[i] is NaN.
data[i] is NaN.
U
Sum( 1.0 / data[i]).
dimension
xx; formerly named ibeta.
igamma.
Cast function.
fromIndex(inclusive) to toIndex(exclusive)
in ascending or descending order.
Cast function.
replaceZeroWith value instead.
Sum( 1.0 / data[i]).
-3 + moment(data,4,mean) / standardDeviation4.
zb2associated with
b2 and the significance, or "p-value" of the kurtosis test statistic zb2, assuming a
two-tailed null hypothesis as well as .
r
between two datasets under the null hypothesis of no correlation.
x and y into
a linear one.start (inclusive) to end (inclusive):
y[0] = start; y[1] = start+1; ...
- linspace(int, int, int) -
Static method in class papaya.Mat
-
- linspace(float, float, float) -
Static method in class papaya.Mat
-
- log(float[]) -
Static method in class papaya.Mat
- Returns an array with each element equal to the natural logarithm (the base-e logarithm)
the corresponding input array.
- log10(float[]) -
Static method in class papaya.Mat
- Returns an array with each element equal to the log 10 value (base 10 logarithm)
of the corresponding input array.
- logGamma(double) -
Static method in class papaya.Gamma
- Returns the natural logarithm of the gamma function; formerly named
lgamma.
- LOGPI -
Static variable in interface papaya.PapayaConstants
-
- logs(float[], int, int) -
Static method in class papaya.Descriptive.Sum
- Returns the sum of logarithms of a data sequence, which is
Sum( Log(data[i]).
- logToBase(float[], float) -
Static method in class papaya.Mat
- Returns an array with each element equal to the log A value
of the corresponding input array.
- LU - Class in papaya
- LU Decomposition.
- LU(float[][]) -
Constructor for class papaya.LU
- Constructor.
U and p-value for
assessing whether one of two samples of independent observations tends to have
larger values than the other.
Sum( data[i] ) / data.length .
k data sequences.
k-th order with constant c of a data sequence,
which is Sum( (data[i]-c)k ) / data.size().
0 through k of the Negative Binomial Distribution.
k+1 to infinity of the Negative
Binomial distribution.
N) necessary to produce a Q-Q plot
for a normal distribution (or normal probability plot).
x (assumes mean is zero, variance is one).
x when the
data has not been standardized (i.e.
x, for which the area under the
Normal (Gaussian) probability density function (integrated from
minus infinity to x) is equal to the argument y
(assumes mean is zero, variance is one); formerly named ndtri.
F = variance between samples / variance within samples.lowerLimit
and more than or equal to the upperLimit
k terms of the Poisson distribution.
k+1 to Infinity of the Poisson distribution.
Sum( (data[i]-c)k );
optimized for common parameters like c == 0.0 and/or k == -2 ..
- powerDeviations(float[], int, float, int, int) -
Static method in class papaya.Descriptive.Sum
- Returns
Sum( (data[i]-c)k ) for all i = from ..
- powers(float[], int) -
Static method in class papaya.Descriptive.Sum
- Returns the sum of powers of a data sequence, which is
Sum ( data[i]k ).
- print(double[], int) -
Static method in class papaya.Mat
- Print the array to the screen in a single line.
- print(double[][], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with each row of the
matrix taking up one line.
- print(double[][], String[], String[], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with the columns and rows labeled according to the
input strings.
- print(float[], int) -
Static method in class papaya.Mat
- Print the array to the screen in a single line.
- print(float[][], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with each row of the
matrix taking up one line.
- print(float[][], String[], String[], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with the columns and rows labeled according to the
input strings.
- print(int[], int) -
Static method in class papaya.Mat
- Print the array to the screen in a single line.
- print(int[][], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with each row of the
matrix taking up one line.
- print(int[][], String[], String[], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with the columns and rows labeled according to the
input strings.
- Probability - Class in papaya
- Cumulative distribution functions and corresponding inverses of certain probability distributions.
- product(int, float) -
Static method in class papaya.Descriptive
- Returns the product, which is
Prod( data[i] ).
- product(float[]) -
Static method in class papaya.Descriptive
- Returns the product of a data sequence, which is
Prod( data[i] ).
- products(float[], float[]) -
Static method in class papaya.Descriptive.Sum
- Returns the sum of the product of two data arrays,
Sum( x[i] * y[i]).
- pValue() -
Method in class papaya.OneWayAnova
- Returns the significance, or "p-value" of the test statistic
OneWayAnova.F().
- pValues(float[]) -
Static method in class papaya.Normality.Dago
- Returns an array containing the three significance, or p-values, for testing normality.
phi-quantile; that is, an element elem
for which holds that phi percent of data elements are less than
elem.
<= element.
nanStrategy and ties
resolved using tiesStrategy.
nanStrategy and ties
resolved using tiesStrategy.
tiesStrategy.
tiesStrategy.
oldValue with
the newValue.
oldValue are replaced with
the newValue.
Delta_i = z_i - y_i,
where
z_i = (slope*x_i + intercept) is the best fit linear line.
Delta_i = z_i - y_i,
where
z_i = (slope*x_i + intercept) is the best fit linear line.
ScatterPlot.setDataExtremes(float, float, float, float)
moment(data,3,mean) / standardDeviation3.
zb1associated with sqrt(b1) and
the significance, or "p-value" of the skew test statistic zb1, assuming a
two-tailed null hypothesis as well as .
rho
between two datasets under the null hypothesis of no correlation.
rho, between multiple columns of a matrix
with each column corresponding to a dataset, and each row an observation.
rho.
t of the Student-t
distribution with k > 0 degrees of freedom.
t, for which the area under the
Student-t probability density function (integrated from
minus infinity to t) is equal to p.
x and y arrays according to
x and going from fromIndex(inclusive) to toIndex(exclusive)
in ascending or descending order.
k data sequences.
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