Instantaneous Incidence Function

Compute the intensity function based on the fitted model. No need to pass parameters as it uses the parameters of the fitted model.

Instantaneous intensity function (event rate) at ``x``.

Fixture for interval censored data

Time by which ``N`` events are expected; the inverse of ``cif``.

Data on the failure of bearings, from [1]_. "Cycles to Failure (millions)" is the number of cycles to failure in millions of cycles. Ref

Returns a Pandas DataFrame containing the data of the tensile strength of Bofors Steel from [2]_. First 5 rows of the dataset: .. l

The Boston house-price data of [3]_. This is a well-known data set used in machine learning. It can be analysed with survival analysis m

Data on the survival of a repairable system from [4]_. References ---------- .. [4] Kaminskiy, M.P. and Krivtsov, V.V. (2010).

Data on the survival of patients who may or may not have received a heart transplant, from [5]_. References ---------- .. [5] Cr

Data on failures of integrated circuits from [12]_. Very difficult for LFP calculations since the data is heavily right censored. R

Data on the survival of a repairable system from [7]_. References ---------- .. [7] Mettas, A. and Zhao, Y.Q. (2005). Mod

Data on the recidivism of released prisoners from [9]_. Includes time varying covariates. References ---------- .. [9] Rossi, P

Data on failures in automotive industry from [11]_. Features heavily (right) censored data. References ---------- .. [11] V.V.

Data on the survival of tires from [10]_. References ---------- .. [10] Krivtsov, V.V., Tananko, D.E., Davis, T.P. (2002).

r""" Calculates the log of the density function of the Gamma distribution at x. .. math:: \log f(x) = \log \left

Natural logarithm of the instantaneous intensity at ``x``.

r""" The k-sample (weighted) log-rank test for the equality of survival distributions of right censored data. At each distinct event time

The mean cumulative function (MCF). For these counting processes the MCF equals the cumulative intensity, so this is a closed-form al

Cause-specific MCF evaluated at ``x`` for the given ``cause``.

Estimate the mean cumulative function at ``x`` for covariates ``Z`` by simulating ``items`` time-terminated sequences out to ``max(x)

Confidence bounds on the cause-specific MCF for ``cause``.

r""" Mean of the Weibull distribution .. math:: E = \alpha \Gamma \left ( 1 + \frac{1}{\beta} \right ) Paramete

r""" Mean of the Logistic distribution .. math:: E = \mu Parameters ---------- mu : numpy arra

r""" Mean of the LogNormal Distribution: .. math:: E = e^{\mu + \frac{\sigma^2}{2}} Parameters --------

r""" Calculates the mean of the Gumbel distribution with given parameters. .. math:: E = \mu - \sigma\gamma Whe

r""" Mean of the LogLogistic distribution .. math:: E = \frac{\alpha \pi / \beta}{sin \left ( \pi / \beta \right )}

r""" Calculates the mean of the Gamma distribution with given parameters. .. math:: E = \frac{\alpha}{\beta} Pa

r""" Mean of the Normal distribution .. math:: E = \mu Parameters ---------- mu : numpy array

r""" Mean of the Binomial distribution: .. math:: E[X] = n p Examples -------- >>> from surpyva