| Package | Description |
|---|---|
| dev.nm.stat.cointegration | |
| dev.nm.stat.distribution.univariate |
Univariate probability distributions, densities, and quantiles.
|
| dev.nm.stat.distribution.univariate.exponentialfamily | |
| dev.nm.stat.evt.evd.univariate |
Univariate extreme-value distributions.
|
| dev.nm.stat.hmm.mixture.distribution | |
| dev.nm.stat.random.rng.univariate | |
| dev.nm.stat.test | |
| dev.nm.stat.test.distribution.kolmogorov | |
| dev.nm.stat.test.distribution.normality | |
| dev.nm.stat.test.distribution.pearson | |
| dev.nm.stat.test.rank.wilcoxon | |
| dev.nm.stat.test.timeseries.adf |
| Modifier and Type | Class and Description |
|---|---|
class |
JohansenAsymptoticDistribution
Johansen provides the asymptotic distributions of the two
hypothesis testings (Eigen and Trace tests), each for 5
different trend types.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BetaDistribution
The beta distribution is the posterior distribution of the parameter p of a binomial
distribution
after observing α - 1 independent events with probability p and
β - 1 with probability 1 - p,
if the prior distribution of p is uniform.
|
class |
BinomialDistribution
The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments,
each of which yields success with probability p.
|
class |
ChiSquareDistribution
The Chi-square distribution is the distribution of
the sum of the squares of a set of statistically independent standard Gaussian random variables.
|
class |
EmpiricalDistribution
An empirical cumulative probability distribution function
is a cumulative probability distribution function that
assigns probability 1/n at each of the n numbers in a sample.
|
class |
ExponentialDistribution
The exponential distribution describes the times between events in a Poisson process,
a process in which events occur continuously and independently at a constant average rate.
|
class |
FDistribution
The F distribution is the distribution of the ratio of two independent chi-squared variates.
|
class |
GammaDistribution
This gamma distribution, when k is an integer, is the distribution of
the sum of k independent exponentially distributed random variables,
each of which has a mean of θ (which is equivalent to a rate parameter of
\(\theta ^{-1}\)).
|
class |
LogNormalDistribution
A log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed.
|
class |
NormalDistribution
The Normal distribution has its density a Gaussian function.
|
class |
PoissonDistribution
The Poisson distribution (or Poisson law of small numbers) is a discrete probability distribution
that expresses the probability of a given number of events occurring in a fixed interval of time
and/or space if these events occur with a known average rate and independently of the time since
the last event.
|
class |
RayleighDistribution
The L2 norm of (x1, x2), where \(x_{i}\)'s are normal, uncorrelated, equal variance and
have the Rayleigh distributions.
|
class |
TDistribution
The Student t distribution is the probability distribution of t, where
\[
t = \frac{\bar{x} - \mu}{s / \sqrt N}
\]
\(\bar{x}\) is the sample mean;
μ is the population mean;
s is the square root of the sample variance;
N is the sample size;
The importance of the Student's distribution is
when (as in nearly all practical statistical work) the population standard deviation is unknown and has to be estimated from the data.
|
class |
TriangularDistribution
The triangular distribution is a continuous probability distribution with lower limit a, upper
limit b and mode c, where a < b and a ≤ c ≤ b.
|
class |
TruncatedNormalDistribution
The truncated Normal distribution is the probability distribution of a normally distributed
random variable whose value is either bounded below or above (or both).
|
class |
WeibullDistribution
The Weibull distribution interpolates between the exponential distribution k = 1 and the
Rayleigh distribution (k = 2),
where k is the shape parameter.
|
| Modifier and Type | Method and Description |
|---|---|
ProbabilityDistribution |
ExponentialFamily.getDistribution(Vector theta)
Construct a probability distribution in the exponential family.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
UnivariateEVD
Distribution of extreme values (e.g., maxima, minima, or other order statistics).
|
| Modifier and Type | Class and Description |
|---|---|
class |
FrechetDistribution
The Fréchet distribution is a special case (Type II) of the generalized extreme value
distribution, with \(\xi>0\).
|
class |
GeneralizedEVD
Generalized extreme value (GEV) distribution is a family of continuous probability distributions
developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families
also known as type I, II and III extreme value distributions.
|
class |
GeneralizedParetoDistribution
Generalized Pareto distribution (GPD) is used for modeling exceedances over (or shortfalls below)
a threshold.
|
class |
GumbelDistribution
The Gumbel distribution is a special case (Type I) of the generalized extreme value distribution,
with \(\xi=0\).
|
class |
MaximaDistribution
The distribution of \(M\), where \(M=\max(x_1,x_2,...
|
class |
MinimaDistribution
The distribution of \(M\), where \(M=\min(x_1,x_2,...
|
class |
OrderStatisticsDistribution
The asymptotic nondegenerate distributions of the r-th smallest (largest) order statistic.
|
class |
ReversedWeibullDistribution
The Reversed Weibull distribution is a special case (Type III) of the generalized extreme value
distribution, with \(\xi<0\).
|
| Constructor and Description |
|---|
MaximaDistribution(ProbabilityDistribution dist,
int nIIDs)
Create an instance with the probability distribution of \(X\), and the number of iid samples
to be drawn.
|
MinimaDistribution(ProbabilityDistribution dist,
int nIIDs) |
OrderStatisticsDistribution(ProbabilityDistribution dist,
int nIIDs,
int order)
Create an instance with the probability distribution of \(X\), the number of iid samples
to be drawn, and the order statistic.
|
| Modifier and Type | Method and Description |
|---|---|
ProbabilityDistribution[] |
PoissonMixtureDistribution.newDistributions() |
ProbabilityDistribution[] |
MixtureDistribution.newDistributions()
Get the distributions (possibly differently parameterized) for all
states.
|
ProbabilityDistribution[] |
BetaMixtureDistribution.newDistributions() |
ProbabilityDistribution[] |
ExponentialMixtureDistribution.newDistributions() |
ProbabilityDistribution[] |
BinomialMixtureDistribution.newDistributions() |
ProbabilityDistribution[] |
LogNormalMixtureDistribution.newDistributions() |
ProbabilityDistribution[] |
GammaMixtureDistribution.newDistributions() |
ProbabilityDistribution[] |
NormalMixtureDistribution.newDistributions() |
| Constructor and Description |
|---|
InverseTransformSampling(ProbabilityDistribution distribution)
Construct a random number generator to sample from a distribution.
|
InverseTransformSampling(ProbabilityDistribution distribution,
RandomLongGenerator uniform)
Construct a random number generator to sample from a distribution.
|
| Modifier and Type | Method and Description |
|---|---|
static double |
HypothesisTest.oneSidedPvalue(ProbabilityDistribution F,
double x)
The one-sided p-value is the probability of observing a test statistic at least as extreme as the one observed.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KolmogorovDistribution
The Kolmogorov distribution is the distribution of the Kolmogorov-Smirnov statistic.
|
class |
KolmogorovOneSidedDistribution
Compute the probability that F(x) is dominated by the upper confidence contour, for all x:
\[
Pn(\epsilon ) = Pr\{F(x) \lt \min\{Fn(x) + \epsilon , 1\}\}
\]
|
class |
KolmogorovTwoSamplesDistribution
Compute the p-values for the generalized (conditionally distribution-free) Smirnov homogeneity test.
|
| Constructor and Description |
|---|
KolmogorovSmirnov1Sample(double[] sample,
ProbabilityDistribution F,
KolmogorovSmirnov.Side side)
Construct a one-sample Kolmogorov-Smirnov test.
|
| Modifier and Type | Class and Description |
|---|---|
class |
JarqueBeraDistribution
Jarque-Bera distribution is the distribution of the Jarque-Bera statistics, which measures the departure from normality.
|
class |
ShapiroWilkDistribution
Shapiro-Wilk distribution is the distribution of the Shapiro-Wilk statistics,
which tests the null hypothesis that a sample comes from a normally distributed population.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FisherExactDistribution
Fisher's exact test distribution is, as its name states, exact, and can therefore be used
regardless of the sample characteristics.
|
| Modifier and Type | Class and Description |
|---|---|
class |
WilcoxonRankSumDistribution
Compute the exact distribution of the Wilcoxon rank sum test statistic.
|
class |
WilcoxonSignedRankDistribution
Compute the exact distribution of the Wilcoxon signed rank test statistic.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ADFAsymptoticDistribution
This class computes the asymptotic distribution of the Augmented Dickey-Fuller (ADF) test
statistic.
|
class |
ADFAsymptoticDistribution1
Deprecated.
use instead
ADFAsymptoticDistribution |
class |
ADFDistribution
This represents an Augmented Dickey Fuller distribution.
|
class |
ADFFiniteSampleDistribution
This class computes the finite sample distribution of the Augmented Dickey-Fuller (ADF) test
statistics.
|
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