Normalise the observation-time argument into a ``{system_id: T}`` mapping, or ``None`` to signal failure-truncated data (each system observed

Log copula mass over the per-row truncation rectangle.

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

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

r""" Confidence bounds of the ``on`` function at the ``alpha_ci`` level of significance. Can be the upper, lower, or two-side

``P(X_other <= x_other | X_d = x_d)`` -- the copula h-function.

Create a Proportional Hazards fitter for the given distribution using exp(beta'Z) as the hazard multiplier. Parameters

Build a covariate design matrix ``Z`` from a pandas DataFrame. Exactly one of ``Z_cols`` or ``formula`` must be provided. Parameters

The probability density function of the fitted model. Parameters ---------- x : array like The values a

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

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

r""" Failure (CDF or unreliability) function for the Uniform Distribution: .. math:: f(x) = \frac{1}{b - a} Par

r""" Density function for the Beta Distribution: .. math:: f(x) = \frac{x^{\alpha-1}\left(1 - x \right )^{\beta-1}}{B \l

r""" Density function for the LogLogistic Distribution: .. math:: f(x) = \frac{\left ( \beta / \alpha \right ) \left ( x

r""" Density function for the Gamma Distribution: .. math:: f(x) = \frac{\beta^{\alpha }}{\Gamma \left ( \alpha \right )

r""" Probability mass function for the Binomial distribution: .. math:: P(X = x) = \binom{n}{x} p^{x} (1 - p)^{n - x}

r""" Density function for the four-parameter Beta distribution: .. math:: f(x) = \frac{\left(x - a\right)^{\alpha - 1}

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

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

r""" Failure (CDF or unreliability) function for the Bernoulli Distribution: .. math:: F(x) = p Parameters

r""" Failure (CDF or unreliability) function for the Uniform Distribution: .. math:: F(x) = \frac{x - a}{b - a}

r""" CDF (or unreliability or failure) function for the Gamma Distribution: .. math:: F(x) = \frac{\gamma \left ( \alpha

r""" CDF (or unreliability or failure) function for the Normal Distribution: .. math:: F(x) = \Phi \left( \frac{x - \mu}

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

The central feature to SurPyval's capability. This function aimed to have an API to mimic the simplicity of the scipy API. That is,

Need to check that causes is the same length TODO: FlemingHarrington baseline.

Fit the generalized renewal model. Parameters ---------- x : array_like An array of event times.

Fits the HPP model to recurrent data and returns the fitted model. Parameters ---------- data : object A

Fit the NHPP model from recurrent data using either Maximum Likelihood Estimation (MLE) or Mean Square Error (MSE) methods.

Fit the generalized renewal model from recurrent data. Parameters ---------- data : RecurrentData Data

Fit the generalized renewal model from recurrent data. Parameters ---------- data : RecurrentData Data

Build a :class:`CopulaModel` from a known parameter and margins.

r""" The central feature to SurPyval's capability. This function aimed to have an API to mimic the simplicity of the scipy API. That i

A method to gather plot data Parameters ---------- heuristic : {'Blom', 'Median', 'ECDF', 'Modal', 'Midpoint', 'Me

Get the times to the first events for each item or subject. In the estimation of recurrent or renewal events it can be helpful to kno

r""" Instantaneous hazard function with the non-parametric estimates from the data. This is calculated using simply the diffe

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

Inverse of the cumulative intensity function (CIF) of the HPP model. Parameters ---------- cif : array_like

Takes the interarrival times from the previous event for a given item and returns the virtual age for each interarrival time. Assumes th