| 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.regression.linear.heteroskedasticity | |
| dev.nm.stat.test.timeseries.adf |
| Class and Description |
|---|
| 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.
|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| 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.
|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| 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.
|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
| Class and Description |
|---|
| 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 and Description |
|---|
| 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.
|
| ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X < x)
\]
Selecting an operation
Use
ProbabilityDistribution.cdf(double) to compute a cumulative probability and
ProbabilityDistribution.quantile(double) to invert it. |
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