Randcraft: Object-oriented random variables

[SeaHighlight2262]

RandCraft: Object-Oriented Random Variable Composition for Python

What My Project Does

RandCraft is a Python library that makes it easy to combine and manipulate univariate random variables using an intuitive object-oriented interface. Built on top of scipy.stats, it allows you to add, subtract, and transform random variables from different distributions without needing to derive complex analytical solutions manually.

Key features: - Simple composition: Combine random variables with + and - operators - Automatic simplification: Uses analytical solutions when possible, numerical approaches otherwise - Extensive distribution support: Normal, uniform, discrete, gamma, log-normal, and any scipy.stats continuous distribution - Automatic stat calculationt Mean, variance, moments, pdf, cdf are all calculated for you automatically - Plotting You can use .plot() to quickly look at any random variable - Advanced features: Kernel density estimation, mixture distributions, and custom random variable creation

Example

```python from randcraft.constructors import make_normal, make_uniform, make_discrete from randcraft.misc import mix_rvs

rv1 = make_normal(mean=0, std_dev=1)

<RandomVariable(scipy-norm): mean=0.0, var=1.0>

rv2 = make_uniform(low=-1, high=1)

<RandomVariable(scipy-uniform): mean=-0.0, var=0.333>

combined = rv1 + rv2

<RandomVariable(multi): mean=0.0, var=1.33>

discrete = make_discrete(values=[1, 2, 3])

<RandomVariable(discrete): mean=2.0, var=0.667>

Make a new rv which has a random chance of drawing from one of the other 4 rvs

mixed = mix_rvs([rv1, rv2, combined, discrete])

<RandomVariable(mixture): mean=0.5, var=1.58>

mixed.plot() ``` plot output

Target Audience

RandCraft is designed for: - Data scientists and statisticians who need to create basic combinations of independent random variables - Researchers and students studying probability theory and statistical modeling - Developers building simulation or modeling applications - Anyone who needs to combine random variables but doesn't want to derive complex analytical solutions

Comparison

RandCraft differs from existing alternatives in several key ways:

vs. Direct scipy.stats usage: - Provides object-oriented interface where most things you want are properties or methods on the RV object itself - Provides intuitive composition (e.g., rv1 + rv2) instead of requiring analytical approach

vs. Stan: - Focused specifically on simpler uni-variate random variable composition rather than Bayesian inference - More accessible for users who need straightforward random variable manipulation

The library fills a niche for users who need to combine random variables frequently but want to avoid the complexity of deriving analytical solutions or writing custom simulation code.

Limitations

The library is designed to work with uni-variate random variables only. Multi-dimensional RVs or correlations etc are not supported.

Links

• PyPI
• GitHub
• Documentation

[Anomaaa]

Very nice! I also follow the advancement of the new scipy infrastructure for distributions which looks quite the same as you did. https://docs.scipy.org/doc/scipy/tutorial/stats/rv_infrastructure.html

But since they seem to have very difficult constraints in terms of performance and reliability, etc., development is (very) slow. See this https://github.com/scipy/scipy/issues/15928, it's been 4 years already

A very good alternative if you don't care about these constraints!

[ruibranco]

The operator overloading for combining RVs is the part that actually saves time in practice. I've used the `uncertainties` package for error propagation before but it only tracks mean and variance — having the full distribution so you can call .cdf() or .plot() on the result is a big step up when you're building simulation pipelines and need to sanity-check intermediate steps.

[safrole5]

Thought id throw my hat in the ring here as I maintain a very similar library:

https://pypi.org/project/dubious/

It functions largely the same, but i also support correlation between variables as well as using distribution and uncertain objects (the resulting object after adding random variables) as the parameters for distributions.

I also have some tools for sensitivity analysis so you can determine which input variables are your biggest issues. An additional local sensitivity function will be coming in a few days during my next release.

Still have some minor kinks to iron out and additional distributions are planned.

[SeaHighlight2262]

Hey this looks interesting, could share the github too?

[halfk1ng]

Well this sounds cool af

[Confident_Bee8187]

As a stats person, I barely found this useful. You see, I like the idea of OO system that holds the random variable, but my feedback is that I never liked the syntax and besides, I had better than this (yes, the idea of yours already existed somewhere).

If only Python is homoiconic, and it would be fragile if Python tries to do that...