r""" The the Corrected Aikake Information Criterion (AIC) for the model, if it was fit with the ``fit()`` method. Not available if fi

Fixture for basic uncensored data

r""" The the Bayesian Information Criterion (BIC) for the model, if it was fit with the ``fit()`` method. Not available if fit with t

Fixture for right censored data

Cumulative Incidence Function

Cumulative intensity (expected event count) by ``x``.

Simulate count-terminated recurrence data based on the fitted model. Parameters ---------- events: int

Simulate count-terminated recurrence data and return the raw events. Like :meth:`count_terminated_simulation` but yields the simulate

The conditional survival function of the fitted model. Parameters ---------- x : array like The values

r""" Conditional survival function for the Weibull Distribution: .. math:: R(x, X) = \frac{R(x + X)}{R(X)} Para

r""" Conditional survival function for the LogNormal Distribution: .. math:: R(x, X) = \frac{R(x + X)}{R(X)} Pa

r""" Survival (or Reliability) function for the Uniform Distribution: .. math:: R(x, X) = \frac{R(x + X)}{R(X)}

r""" Conditional survival (or reliability) function for the Beta Distribution: .. math:: R(x, X) = \frac{R(x + X

r""" Conditional survival function for the LogLogistic Distribution: .. math:: R(x, X) = \frac{R(x + X)}{R(X)}

r""" Conditional survival function for the Gamma Distribution: .. math:: R(x) = e^{-\lambda x} Parameters

r""" Conditional survival function for the Normal Distribution: .. math:: R(x, X) = \frac{R(x + X)}{R(X)} Param

r""" Conditional survival; the probability of surviving a further ``x`` events having already survived ``X``: .. math::

r""" Conditional survival function for the Exponential Distribution: .. math:: R(x) = e^{-\lambda x} The Expone

r""" Conditional survival (or reliability) function for the four-parameter Beta distribution: .. math:: R(x, X)

r""" Conditional survival function for the Rayleigh Distribution: .. math:: R(x, X) = \frac{R(x + X)}{R(X)} Par

r""" Conditional survival (or reliability) function for the ExpoWeibull Distribution: .. math:: R(x, X) = \frac{

r""" Density function with the non-parametric estimates from the data. This is calculated using the relationship between the

r""" Density function for the Weibull Distribution: .. math:: f(x) = \frac{\beta}{\alpha} \frac{x}{\alpha}^{\beta - 1} e

r""" Density function (pdf) for the Gumbel Distribution: .. math:: f(x) = \frac{1}{\sigma}e^{\left (\frac{x - \mu}{\sigm

r""" Density function for the Normal Distribution: .. math:: f(x) = \frac{1}{\sigma \sqrt{2\pi}}e^{-\frac{1}{2}\left ( \

r""" Density function for the Exponential Distribution: .. math:: f(x) = \lambda e^{-\lambda x} Parameters

r""" Density function for the Rayleigh Distribution: .. math:: f(x) = \frac{x}{\sigma^2} e^{-\frac{x^2}{2\sigma^2}}

r""" The density function for a distribution using the parameters found in the ``.params`` attribute. Parameters ----

r""" Calculates the entropy of the Logistic distribution. .. math:: S = \ln \left ( \sigma \right ) + 2 Paramet

r""" Calculates the entropy of the LogNormal distribution. .. math:: S = \mu + \frac{1}{2} \ln \left ( 2\pi e \sigma^{2}

r""" Calculates the entropy of the Uniform distribution. .. math:: S = \ln \left ( b - a \right ) Parameters

r""" Calculates the entropy of the Gumbel distribution. .. math:: S = \ln \left ( \sigma \right ) + \gamma + 1

r""" Calculates the entropy of the Beta distribution. .. math:: S = \ln B \left ( \alpha, \beta \right ) - \

r""" Calculates the entropy of the LogLogistic distribution. .. math:: S = \ln \left ( \frac{\alpha}{\beta} \right ) + 2

r""" Calculates the entropy of the Gamma distribution. .. math:: S = \alpha - \ln \left ( \beta \right ) + \ln \Gamma \l

r""" Calculates the entropy of the Normal distribution. .. math:: S = \frac{1}{2} \ln \left ( 2\pi e \sigma^{2} \right )

r""" Entropy of the Binomial distribution (in nats). Examples -------- >>> from surpyval import Binomial >>>

r""" Calculates the entropy of the Exponential distribution. .. math:: S = 1 - \ln \left ( \lambda \right ) Par

r""" Differential entropy of the four-parameter Beta distribution. Equal to the standard Beta entropy plus :math:`\ln(b - a)` for th

r""" Calculates the entropy of the ExpoWeibull distribution. The entropy of the ExpoWeibull distribution has no closed form

r""" Calculates the entropy of the Gumbel LEV distribution. .. math:: S = \ln \left ( \sigma \right ) + \gamma + 1

The distinct event types (marks) present in the data, excluding the ``None`` mark used for censored / end-of-observation rows. Return

r""" CDF (failure or unreliability) function with the non-parametric estimates from the data Parameters ----------

r""" Failure (CDF or unreliability) function for the Logistic Distribution: .. math:: F(x) = \\frac{1}{1 + e^{- \\left (

r""" CDF (or Failure) function for the Gumbel Distribution: .. math:: F(x) = e^{e^{-\left ( x - \mu \right )/\sigma}}

r""" Failure (CDF or unreliability) function for the LogLogistic Distribution: .. math:: F(x) = \frac{1}{1 + \le

r""" Failure (CDF) function for the Binomial distribution: .. math:: F(x) = P(X \leq x) = \sum_{i=0}^{\lfloor x \rfloor}

r""" CDF (or unreliability or failure) function for the Exponential Distribution: .. math:: F(x) = 1 - e^{-\lamb