Anticpy

**Main enhancement:**

- Add a batch-wise implementation of the change point analysis algorithm to avoid memory errors in the case of a high amount of data and possible change point configurations.

**Bug fixes:**
- Fix an index error in the change point probability normalization via a Riemann sum of rectangles.

The release contains bug fixes for the ``LangevinEstimation`` class:

- The attributes ``drift_model`` and ``diffusion_model`` were missing.
- The error if the noise level or the transition marker were not given is fixed.
- The documenation is adapted.

The release contains bug fixes for the ``CPSegmentFit`` class:

- The print output is adapted.
- The division by zero error for some ``normalizing_Z_factor`` values is fixed.
- The sometimes unstable integration via the Simpson rule is complemented with the simple summation of values.

The release contains bug fixes for the ``LangevinEstimation`` class:

- The attributes ``drift_model`` and ``diffusion_model`` were missing.
- The error if the noise level or the transition marker were not given is fixed.
- The documenation is adapted.

The release contains bug fixes for the ``CPSegmentFit`` class:

- The print output is adapted.
- The division by zero error for some ``normalizing_Z_factor`` values is fixed.
- The sometimes unstable integration via the Simpson rule is complemented with the simple summation of values.

This beta-release v0.0.2 contains

- the Markov Chain Monte Carlo algorithm to estimate the drift-slope as resilience measure and the noise level of a Langevin model from time series data in sliding windows,
- the option to create an animation of the sliding window procedure,
- methods to calculate change point probabilities
- and functions to create a Bayesian non-parametric, segmental linear fit to extrapolate the time horizon of an upcoming transition based on the current information and model assumptions.

The algorithms are described in the related publication "Bayesian on-line estimation of critical transitions". The general functionalities can also be used to reproduce the results of the publication. The simulated data sets of the article can be found in the folder "tutorial_data" of the related github repository.

Initial release of a beta version.