GeneralizedExtremeValue
GeneralizedExtremeValue()Attributes
| Name | Description |
|---|---|
| location | |
| parameters_valid | |
| scale | |
| shape |
Methods
| Name | Description |
|---|---|
| cdf | cdf(self: corehydropy._core.GeneralizedExtremeValue, x: typing.SupportsFloat | typing.SupportsIndex) -> float |
| kurtosis | kurtosis(self: corehydropy._core.GeneralizedExtremeValue) -> float |
| linear_moments_from_parameters | linear_moments_from_parameters(self: corehydropy._core.GeneralizedExtremeValue, parameters: collections.abc.Sequence[typing.SupportsFloat | typing.SupportsIndex]) -> list[float] |
| log_likelihood | log_likelihood(self: corehydropy._core.GeneralizedExtremeValue, sample: collections.abc.Sequence[typing.SupportsFloat | typing.SupportsIndex]) -> float |
| maximum | maximum(self: corehydropy._core.GeneralizedExtremeValue) -> float |
| mean | mean(self: corehydropy._core.GeneralizedExtremeValue) -> float |
| median | median(self: corehydropy._core.GeneralizedExtremeValue) -> float |
| minimum | minimum(self: corehydropy._core.GeneralizedExtremeValue) -> float |
| mode | mode(self: corehydropy._core.GeneralizedExtremeValue) -> float |
| parameter_covariance | parameter_covariance(self: corehydropy._core.GeneralizedExtremeValue, sample_size: typing.SupportsInt | typing.SupportsIndex) -> list[list[float]] |
| pdf(self: corehydropy._core.GeneralizedExtremeValue, x: typing.SupportsFloat | typing.SupportsIndex) -> float | |
| quantile | quantile(self: corehydropy._core.GeneralizedExtremeValue, p: typing.SupportsFloat | typing.SupportsIndex) -> float |
| quantile_gradient | quantile_gradient(self: corehydropy._core.GeneralizedExtremeValue, p: typing.SupportsFloat | typing.SupportsIndex) -> list[float] |
| quantile_variance | quantile_variance(self: corehydropy._core.GeneralizedExtremeValue, p: typing.SupportsFloat | typing.SupportsIndex, sample_size: typing.SupportsInt | typing.SupportsIndex) -> float |
| skewness | skewness(self: corehydropy._core.GeneralizedExtremeValue) -> float |
| standard_deviation | standard_deviation(self: corehydropy._core.GeneralizedExtremeValue) -> float |
cdf
GeneralizedExtremeValue.cdf()cdf(self: corehydropy._core.GeneralizedExtremeValue, x: typing.SupportsFloat | typing.SupportsIndex) -> float
kurtosis
GeneralizedExtremeValue.kurtosis()kurtosis(self: corehydropy._core.GeneralizedExtremeValue) -> float
linear_moments_from_parameters
GeneralizedExtremeValue.linear_moments_from_parameters()linear_moments_from_parameters(self: corehydropy._core.GeneralizedExtremeValue, parameters: collections.abc.Sequence[typing.SupportsFloat | typing.SupportsIndex]) -> list[float]
log_likelihood
GeneralizedExtremeValue.log_likelihood()log_likelihood(self: corehydropy._core.GeneralizedExtremeValue, sample: collections.abc.Sequence[typing.SupportsFloat | typing.SupportsIndex]) -> float
maximum
GeneralizedExtremeValue.maximum()maximum(self: corehydropy._core.GeneralizedExtremeValue) -> float
mean
GeneralizedExtremeValue.mean()mean(self: corehydropy._core.GeneralizedExtremeValue) -> float
median
GeneralizedExtremeValue.median()median(self: corehydropy._core.GeneralizedExtremeValue) -> float
minimum
GeneralizedExtremeValue.minimum()minimum(self: corehydropy._core.GeneralizedExtremeValue) -> float
mode
GeneralizedExtremeValue.mode()mode(self: corehydropy._core.GeneralizedExtremeValue) -> float
parameter_covariance
GeneralizedExtremeValue.parameter_covariance()parameter_covariance(self: corehydropy._core.GeneralizedExtremeValue, sample_size: typing.SupportsInt | typing.SupportsIndex) -> list[list[float]]
GeneralizedExtremeValue.pdf()pdf(self: corehydropy._core.GeneralizedExtremeValue, x: typing.SupportsFloat | typing.SupportsIndex) -> float
quantile
GeneralizedExtremeValue.quantile()quantile(self: corehydropy._core.GeneralizedExtremeValue, p: typing.SupportsFloat | typing.SupportsIndex) -> float
quantile_gradient
GeneralizedExtremeValue.quantile_gradient()quantile_gradient(self: corehydropy._core.GeneralizedExtremeValue, p: typing.SupportsFloat | typing.SupportsIndex) -> list[float]
quantile_variance
GeneralizedExtremeValue.quantile_variance()quantile_variance(self: corehydropy._core.GeneralizedExtremeValue, p: typing.SupportsFloat | typing.SupportsIndex, sample_size: typing.SupportsInt | typing.SupportsIndex) -> float
skewness
GeneralizedExtremeValue.skewness()skewness(self: corehydropy._core.GeneralizedExtremeValue) -> float
standard_deviation
GeneralizedExtremeValue.standard_deviation()standard_deviation(self: corehydropy._core.GeneralizedExtremeValue) -> float