r""" Failure (CDF or unreliability) function for the Rayleigh Distribution: .. math:: F(x) = 1 - e^{-\frac{x^2}{

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

A lot of commentary about this being difficult to interpret. In engineering this is not the case, eliminating the failure wil

r""" The cumulative distribution function, or failure function, for a distribution using the parameters found in the ``.params`` attri

Method From: Filliben, J. J. (February 1975), "The Probability Plot Correlation Coefficient Test for Normality", Technometrics, Ameri

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

r""" This function aimed to have an API to mimic the simplicity of the scipy API. That is, to use a simple :code:`fit()` call,

Fit the proportional hazards model to the data. Parameters ---------- x : array_like The observed event

Fit a nonparametric (Nelson-Aalen) MCF. Parameters ---------- x : array like Event (and censoring) times

Fits the HPP model to the provided data and returns the fitted model. Parameters ---------- x : array_like

Fit the NHPP model from the provided data. This function prepares the data to ensure that it is in the correct format for the fitting

Fit a cause-specific MCF. Parameters ---------- x : array like Event (and censoring) times. i :

Fit the model using the provided data and initial parameters (if given) Parameters ---------- x : array_like

Fit the model using the provided data and initial parameters. Parameters ---------- x : array_like Inpu

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

Fit the ARI model. Parameters ---------- x : array_like An array of event times. i : array_like

Fit the regression model using a pandas DataFrame as the input. The names of the covariates are retained on the fitted model so that

Fits a Cox PH model using a pandas dataframe as the input. Parameters ---------- df: pandas.DataFrame T

Fit the generalized renewal model from given parameters. Parameters ---------- params : list A list of

Fit the generalized renewal model from given parameters. Parameters ---------- params : list A list of

Build an ARI model from given parameters. Parameters ---------- dist_params : list Parameters for the b

Variance of the Fleming-Harrington cumulative hazard estimator using the same tie correction as the estimator itself: Var(H) = sum(sum(1

r""" Creating a SurPyval Parametric class with provided parameters. Parameters ---------- params : array like

r""" Create a Binomial model from the parameters ``[n, p]``. Parameters ---------- params : array like

Converts the fs format to the xrd format. Parameters ---------- f: array array of values for which the failure/death was obs

Get all events for a specific item or subject. Parameters ---------- item : int or str The id of the ite

Greenwood's formula for the variance of the cumulative hazard (equivalently, of -log(R)) of the Kaplan-Meier estimator: Var(H) = sum(d /

r""" Instantaneous hazard rate for the Weibull Distribution: .. math:: h(x) = \frac{\beta}{\alpha} \left ( \frac{x}{\alp

r""" Instantaneous hazard rate for the Logistic Distribution: .. math:: h(x) = \\frac{f(x)}{R(x)} Parameters

r""" Instantaneous hazard rate for the LogNormal Distribution: .. math:: h(x) = \frac{f(x)}{R(x)} Parameters

r""" Instantaneous hazard rate for the Uniform Distribution: .. math:: h(x) = \frac{1}{b - x} Parameters

r""" Instantaneous hazard rate for the Gumbel Distribution: .. math:: h(x) = \frac{1}{\sigma} e^{\frac{x-\mu}{\sigma}}

r""" Instantaneous hazard rate for the Beta distribution. .. math:: h(x) = \frac{f(x)}{R(x)} Parameters

r""" Instantaneous hazard rate for the LogLogistic Distribution: .. math:: h(x) = \frac{f(x)}{R(x)} Parameters

r""" Instantaneous hazard rate for the Gamma Distribution: .. math:: h(x) = \frac{\frac{\beta^{\alpha }}{\Gamma \left (

r""" Instantaneous hazard rate for the Normal Distribution: .. math:: h(x) = \frac{\frac{1}{\sigma \sqrt{2\pi}}e^{-\frac

r""" Discrete hazard rate for the Binomial distribution; the conditional probability of exactly ``x`` events given at least ``x``:

r""" Instantaneous hazard rate for the Exponential Distribution. .. math:: f(x) = \lambda The failure rate for

r""" Instantaneous hazard rate for the four-parameter Beta distribution. .. math:: h(x) = \frac{f(x)}{R(x)}

r""" Instantaneous hazard rate for the Rayleigh Distribution: .. math:: h(x) = \frac{x}{\sigma^2} Parameters

r""" Instantaneous hazard rate for the ExpoWeibull Distribution: .. math:: h(x) = \frac{f(x)}{R(x)} Parameters

r""" Instantaneous hazard rate for the Gumbel LEV Distribution: .. math:: h(x) = \frac{f(x)}{R(x)} Parameters

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