jiayaobo/stamox

make your statistical research faster

stamox's Issues

Several Main Topics For Stamox

1. Separate methods to diffrent apis for pipe and non-pipe functions
Which means, if we want to create two suite of api function and pipe_function, for function are all vanilla functions and pipe_function are all decorated with make_pipe or make_partial_pipe like series apis. So users can import stamox.function as F or import stamox.pipe_function as PF to achieve all methods
2. A better_partial
A better_partial is more free version of functools.partial for latter, code below will raise an error

f = lambda x, y: x+y
f_partial = partial(f, x=1)
f_partial(1)

But for better_partial, it's possible to do

def f(a, b, c, d):
    return a * b + c * d

g = better_partial(f, b=2, c=3)
g(1,2) == 8

3. merge partial_pipe and pipe decoraters
Which means users don't need to consider whether to use make_partial_pipe or make_pipe

Workflow has been configured

a simple workflow for python 3.8, 3.9 and 3.10 has been configured

Autocorrelation and autocovariance

Hi @JiaYaobo

Stamox looks like a really nice project and I am looking for a JAX-compatible library that implements some more statistical tools than what is offered through the numpy and scipy interfaces.

Specifically I am looking for an implementation of the acf function from statsmodels: https://github.com/statsmodels/statsmodels/blob/0407c4e71c43766c155ed8e1f8ea5c3fbab035b1/statsmodels/tsa/stattools.py#L577

Would you be interested in implementing this in stamox? If I manage to find the time I would be happy to do a PR for this

Things to do

WIP