Lmo

What's Changed
* Bump matplotlib from 3.7.2 to 3.8.0 by dependabot in https://github.com/jorenham/Lmo/pull/32
* Bump typing-extensions from 4.7.1 to 4.8.0 by dependabot in https://github.com/jorenham/Lmo/pull/33
* Bump pyright from 1.1.326 to 1.1.327 by dependabot in https://github.com/jorenham/Lmo/pull/34
* Bump ruff from 0.0.288 to 0.0.290 by dependabot in https://github.com/jorenham/Lmo/pull/36
* Bump hypothesis from 6.84.3 to 6.86.2 by dependabot in https://github.com/jorenham/Lmo/pull/37
* fixed typos by jorenham in https://github.com/jorenham/Lmo/pull/46
* Fixed the L-coratio definitions in the documentation by jorenham in https://github.com/jorenham/Lmo/pull/47


**Full Changelog**: https://github.com/jorenham/Lmo/compare/v0.10.1...v0.10.2

What's Changed
* Theoretical L-comoments by jorenham in https://github.com/jorenham/Lmo/pull/30
* Bump actions/checkout from 3 to 4 by dependabot in https://github.com/jorenham/Lmo/pull/31

New Contributors
* dependabot made their first contribution in https://github.com/jorenham/Lmo/pull/31

**Full Changelog**: https://github.com/jorenham/Lmo/compare/v0.10.0...v0.10.1

What's Changed
* Added more linting, fixed many code style issues and a few typos by wolph in https://github.com/jorenham/Lmo/pull/13
* L-moment covariance matrix from CDF by jorenham in https://github.com/jorenham/Lmo/pull/14
* L-stats covariance matrix from CDF by jorenham in https://github.com/jorenham/Lmo/pull/15
* Calculate theoretical L-* from `scipy.stats.rv_continuous` distributions by jorenham in https://github.com/jorenham/Lmo/pull/16
* Python 3.12 support by jorenham in https://github.com/jorenham/Lmo/pull/17
* Tighter bounds on the (absolute) L-moment and L-ratio by jorenham in https://github.com/jorenham/Lmo/pull/18
* Improved L-stats API by jorenham in https://github.com/jorenham/Lmo/pull/19
* Goodness-of-fit testing of any distribution, using L-moments or L-stats by jorenham in https://github.com/jorenham/Lmo/pull/20
* Standardized optional `scipy.integrate.quad` options in `lmo.theoretical` by jorenham in https://github.com/jorenham/Lmo/pull/21
* Theoretical influence functions & robustness measures by jorenham in https://github.com/jorenham/Lmo/pull/22
* `lmo.theoretical`: Discrete distributions, numerical accuracy improvements, and cleaner code by jorenham in https://github.com/jorenham/Lmo/pull/25
* Add theoretical L-* methods to all `scipy.stats` univariate distributions by jorenham in https://github.com/jorenham/Lmo/pull/26
* Method of L-moments (LMM) by jorenham in https://github.com/jorenham/Lmo/pull/27
* Empirical influence functions by jorenham in https://github.com/jorenham/Lmo/pull/28
* Allow passing `trim=t` as alias for `trim=(t, t)` by jorenham in https://github.com/jorenham/Lmo/pull/29

New Contributors
* wolph made their first contribution in https://github.com/jorenham/Lmo/pull/13

**Full Changelog**: https://github.com/jorenham/Lmo/compare/v0.9.0...v0.10.0

What's Changed
* Non-parameteric continuous distribution from L-moments by jorenham in https://github.com/jorenham/lmo/pull/12, see [`lmo.l_rv`](https://jorenham.github.io/lmo/api/#lmo.l_rv).


**Full Changelog**: https://github.com/jorenham/lmo/compare/v0.8.0...v0.9.0

- A novel generalization of trimmed L-moments and L-comoments: Fractional trimming 🎉 . Trim lengths can now be any positive float. (11)
- New convenience methods: `lmo.l_stats`, `lmo.l_costats`, and `lmo.theoretical.l_(ratio|stats)_from_(cdf|ppf)`
- New low-level `lmo.ostats` module, for internally used (fractional) order-statistics calculations.
- Improved function typing with `typing.overload` .
- Improved parameter typing by annotating `**kwargs` with `TypedDict`.
- Many documentation improvements.
- Reduced test flakyness, removed redundant tests, and added some new ones.
- Made the internal low-level `_pwm` module public, by renaming it to `pwm_beta`.
- BREAKING: most `dtype` and `axis` kwargs are now keyword-only arguments.

**Full Changelog**: https://github.com/jorenham/lmo/compare/v0.7.0...v0.8.0

- Improved README.md [1, 2]
- Theoretical L-moments from univariate continuous distributions, using either a CDF of PPF [6]
- Improved numerical stability of sample L-(co)moments with several orders of magnitude
- General documentation improvements
- Improved code style, by enforcing a stricter ruff ruleset
- [BREAKING] Bumped minimum numpy version to 1.22
- [BREAKING] Bumped minimum scipy version to 1.9

**Full Changelog**: https://github.com/jorenham/lmo/compare/v0.6.1...v0.7.0