Joint density ``c(F_1, F_2) f_1 f_2``.

Calculate the life parameter for a distribution using the covariates / stresses, Z, and the parameters of the dual exponential model.

Initialize the parameters of the dual exponential model for the initial guess of the optimization. Parameters: -----

r""" Creates a plot of the survival function. Two-sided confidence bounds are drawn as a shaded band in the same colour as th

A method to do a probability plot Parameters ---------- heuristic : {'Blom', 'Median', 'ECDF', 'Modal', 'Midpoint',

r""" A method to plot the survival function, cumulative hazard function, hazard function and density function of the distribution usi

Overlay the simulated MCF on the empirical MCF of the fitted data. Parameters ---------- ax: matplotlib axes, optio

Compute the inverse of the cumulative incidence function (CIF) based on the fitted model, if it's defined for the distribution.

Overlay the MCF of every cause on a single axis.

PLots the CIF of the model against the data used to fit it. To do this, the plot method takes the average of the covariates, and

r""" Quantile function for the Weibull distribution: .. math:: q(p) = \alpha \left ( -\ln \left ( 1 - p \right ) \right

r""" Quantile function for the Logistic distribution: .. math:: q(p) = \\mu + \\sigma \\ln \\left ( \\frac{p}{1 - p} \\r

r""" Quantile function for the Uniform Distribution: .. math:: q(p) = a + p(b - a) Parameters ---------

r""" Quantile function for the Gumbel Distribution: .. math:: q(p) = \mu + \sigma\ln\left ( -\ln\left ( 1 - p \right ) \

r""" Quantile function for the LogLogistic distribution: .. math:: q(p) = \alpha \left ( \frac{p}{1 - p} \right )^{\frac

r""" Quantile function for the Gamma Distribution: .. math:: q(p) = \frac{-\ln\left ( p \right )}{\lambda} Para

r""" Quantile (inverse CDF) function for the Binomial distribution; the smallest number of events ``x`` such that :math:`F(x) \geq q`

r""" Quantile function for the Exponential Distribution: .. math:: q(p) = \frac{-\ln\left ( 1 - p \right )}{\lambda}

r""" Quantile function for the Rayleigh distribution: .. math:: q(p) = \sigma \sqrt{-2 \ln \left ( 1 - p \right )}

r""" Instantaneous hazard rate for the ExpoWeibull Distribution: .. math:: q(p) = Parameters ----------

r""" Quantile function for the Gumbel LEV Distribution: .. math:: q(p) = \mu - \sigma\ln\left ( -\ln\left ( p \right ) \

r""" Draws random samples from the fitted distribution. Each observed value x is drawn with the probability mass the estimated surviva

r""" Draws random samples from the distribution in shape `size`, using the inverse transform method with the distribution's quantile

r""" A method to draw random samples from the distributions using the parameters found in the ``.params`` attribute. Paramet

r""" Draws random samples from the distribution in shape `size` Parameters ---------- size : integer or tuple of po

r""" Draws random samples from the distribution in shape `size` Parameters ---------- size : integer or tuple of po

r""" A method to draw random samples from the distributions using the parameters found in the ``.params`` attribute. Paramet

Draw correlated samples; returns an array of shape ``(size, 2)``.

r""" Survival (or reliability) function for the Bernoulli Distribution: .. math:: R(x) = 1 - p Parameters

Returns the ensemble survival function Parameters ---------- x : int | float | ArrayLike Time samples Z :

Joint survival ``P(X_1 > x_1, X_2 > x_2)``.

Bind the decorated class' name to a single configured instance. Use on a fitter whose ``__init__`` takes no required arguments. The name is r

Calculate the minimum success probability of a run of 'n' independent events for a given confidence level. Useful when you want to know, with

Transforms surpyval's xZc format to scikit-survival's Xy format.

Test adding covariates