std-dev 0.1.0

Your Swiss Army knife for swiftly processing any amount of data. Implemented for industrial and educational purposes alike.
Documentation
-   [x] Change precision in output.
-   [x] Arbitrary precision for high degree polynomials.
-   [x] Automatically choose process
-   [x] Linearization - solve all other.
-   [x] Fix input handling? (arrow up, sides, don't spam prompt when pasting (or don't have prompt on multiline?))
-   [x] Fix NaN on exponential and power regressions (add the min y + 1 to get all numbers above 1, then fit the curve to that)
-   [x] Slow ( O(n²) ) Theil-Sen estimator
-   [ ] O(n log n) Theil-Sen estimator (heavy-hitter, very hard)
-   [x] O(n) median (intermediate difficulty)
-   [x] All statistical tools for unique lists (not `Cluster`s)
-   [x] Performance logging in regression calculations.
-   [x] Plotting of data & regressions using [poloto]https://crates.io/crates/poloto or [plotlib]https://crates.io/crates/plotlib
-   [ ] Option for other plot lib.
-   [ ] Fix [bias]https://en.wikipedia.org/wiki/Nonlinear_regression#Transformation in power and exponential regressions.
        Right now, it's biased towards errors in small values, as the large errors are, in the linear space, the log of what they are in reality.
-   [ ] [RANSAC](https://en.wikipedia.org/wiki/Random_sample_consensus) implementation? Iterations = lg(1-chance of success) \* (lg(number of data points) / lg(outliers in relation to total))
-   [ ] Support [covariance]https://en.wikipedia.org/wiki/Generalized_least_squares, for better estimation.
-   [ ] [Non](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)-[parametric](https://en.wikipedia.org/wiki/Local_regression) regression?
-   [ ] [Non-linear regression?](https://en.wikipedia.org/wiki/Non-linear_least_squares)