scikitplot.stats#

Elegant statistical tools for intuitive and insightful data visualization and interpretation.

The stats module offers a wide range of probability distributions, summary and frequency statistics, correlation functions, statistical tests, masked statistics, and additional tools.

User guide. See the Stats (experimental) section for further details.

Astrostatistics: Bayesian Blocks for Time Series Analysis#

User guide. See the Astropy (experimental) section for further details.

`Events`	Bayesian blocks fitness for binned or unbinned events.
`FitnessFunc`	Base class for bayesian blocks fitness functions.
`PointMeasures`	Bayesian blocks fitness for point measures.
`RegularEvents`	Bayesian blocks fitness for regular events.
`bayesian_blocks`	Compute optimal segmentation of data with Scargle's Bayesian Blocks.

Astrostatistics Tools#

This module contains simple statistical algorithms that are straightforwardly implemented as a single python function (or family of functions).

This module should generally not be used directly. Everything in __all__ is imported into astropy.stats, and hence that package should be used for access.

User guide. See the Astropy (experimental) section for further details.

`binned_binom_proportion`	Binomial proportion and confidence interval in bins of a continuous variable `x`.
`binom_conf_interval`	Binomial proportion confidence interval given k successes, n trials.
`bootstrap`	Performs bootstrap resampling on numpy arrays.
`cdf_from_intervals`	Construct a callable piecewise-linear CDF from a pair of arrays.
`fold_intervals`	Fold the weighted intervals to the interval (0,1).
`gaussian_fwhm_to_sigma`	Convert a string or number to a floating point number, if possible.
`gaussian_sigma_to_fwhm`	Convert a string or number to a floating point number, if possible.
`histogram_intervals`	Histogram of a piecewise-constant weight function.
`interval_overlap_length`	Compute the length of overlap of two intervals.
`kuiper`	Compute the Kuiper statistic.
`kuiper_false_positive_probability`	Compute the false positive probability for the Kuiper statistic.
`kuiper_two`	Compute the Kuiper statistic to compare two samples.
`mad_std`	Calculate a robust standard deviation using the median absolute deviation (MAD).
`median_absolute_deviation`	Calculate the median absolute deviation (MAD).
`poisson_conf_interval`	Poisson parameter confidence interval given observed counts.
`signal_to_noise_oir_ccd`	Computes the signal to noise ratio for source being observed in the optical/IR using a CCD.

Astrostatistics: Selecting the bin width of histograms#

`calculate_bin_edges`	Calculate histogram bin edges like `numpy.histogram_bin_edges`.
`freedman_bin_width`	Return the optimal histogram bin width using the Freedman-Diaconis rule.
`histogram`	Enhanced histogram function, providing adaptive binnings.
`knuth_bin_width`	Return the optimal histogram bin width using Knuth's rule.
`scott_bin_width`	Return the optimal histogram bin width using Scott's rule.

Astrostatistics: Model Selection#

This module contains simple functions for model selection.

User guide. See the Astropy (experimental) section for further details.

`akaike_info_criterion`	Computes the Akaike Information Criterion (AIC).
`akaike_info_criterion_lsq`	Computes the Akaike Information Criterion assuming that the observations are Gaussian distributed.
`bayesian_info_criterion`	Computes the Bayesian Information Criterion (BIC) given the log of the likelihood function evaluated at the estimated (or analytically derived) parameters, the number of parameters, and the number of samples.
`bayesian_info_criterion_lsq`	Computes the Bayesian Information Criterion (BIC) assuming that the observations come from a Gaussian distribution.

Discrete Distributions Tools#

Tweedie Distribution Module#

This module implements the Tweedie distribution, a member of the exponential dispersion model (EDM) family, using SciPy’s rv_continuous class.

It is especially useful for modeling claim amounts in the insurance industry, where data often exhibit a mixture of zeroes and positive continuous values.

The primary focus of this package is the compound-Poisson behavior of the Tweedie distribution, particularly in the range 1 < p < 2. However, it supports calculations for all valid values of the shape parameter p.

Notes

The probability density function (PDF) of the Tweedie distribution cannot be expressed in a closed form for most values of p. However, approximations and numerical methods are employed to compute the PDF for practical purposes.

The Tweedie distribution family includes several well-known distributions based on the value of the shape parameter p:

p = 0 : Normal distribution
p = 1 : Poisson distribution
1 < p < 2 : Compound Poisson-Gamma distribution
p = 2 : Gamma distribution
2 < p < 3 : Positive stable distributions
p = 3 : Inverse Gaussian distribution
p > 3 : Positive stable distributions

The Tweedie distribution is undefined for values of p in the range (0, 1).

References

[1] Jørgensen, B. (1987). “Exponential dispersion models”.: Journal of the Royal Statistical Society, Series B. 49 (2): 127–162.
[2] Tweedie, M. C. K. (1984). “An index which distinguishes between some important exponential families”.: In Statistics: Applications and New Directions. Proceedings of the Indian Statistical Institute Golden Jubilee International Conference.
[3] [YouTube]: Statistical Methods Series: Zero-Inflated GLM and GLMM.
[4] [Google]: https://www.statisticshowto.com/tweedie-distribution/

`tweedie_gen`	A Tweedie continuous random variable inherited `scipy.stats.rv_continuous`.
`tweedie`	An instance of `tweedie_gen`, providing Tweedie distribution functionality.