Wildboottest

[Diff since v0.6.0](https://github.com/droodman/WildBootTests.jl/compare/v0.6.0...v0.6.1)

Implementation seems pretty mature. First time creating a registered package.

- Relaxes version requirements of multiple dependencies.

- Upgrades the required `numba` version to `0.57` so that `wildboottest` can run with Python `3.11`.
- Parallelizes some `numba` for loops and adds a `parallel` argument to `wildboottest`, which is `True` by default.

With version `0.1.7`, it is possible to run a non-clustered wild bootstrap. Here is a brief example:

python
import pandas as pd
import statsmodels.formula.api as sm
from wildboottest.wildboottest import wildboottest

df = pd.read_csv("https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/sandwich/PetersenCL.csv")
model = sm.ols(formula='y ~ x', data=df)
wildboottest(model, param = "x", B = 999)
| param | statistic | p-value |
|:--------|------------:|----------:|
| x | 36.448 | 0.000 |