semvec/pypi-packages huggingface dataset:
causality-ml 0.0.1
Yet Another Causality Library
Requires Python: >=3.10

# Causality

Welcome to the causality library!  Yet another causality library for doing causal inference.  This library is nothing special and doesn't intend to be.  It's really just for me, so I can build the interfaces I want.  Use it if you want to, it has an MIT license.  Don't if you don't want to!

There are lots of great causality frameworks out there:
* causalml
* causaldiscovery
* causalens
* do-why
* bn-learn 

Also please checkout [this](https://github.com/matteocourthoud/awesome-causal-inference/blob/main/src/libraries.md) repo which has a bunch more!

## How To Install

`python -m pip install causality`

## Erreta

Currently this library is in alpha release.  It's mostly just design and a few ideas at this point.  Please don't take anything in here too seriously.  I plan to flesh this thing out over time.  Right now it's more or less a wrapper with some reference implementations.  But the plan is to really turn this into something special.

I think either I want this to be written in mostly C & Cython.  Or maybe jax?  Which I think could be super super cool.  Ideally there are interfaces for getting p-values and confidence intervals for every single thing that does statistics.  Don't ask me how that will work yet.  Also, I'd like to include bayesian credible intervals and bayes factor.  I think it'd also be great to include permutation and boot straps where possible.  Just really be able to analyze everything from every direction.  Maybe even some SHAP, LIME and other related interpretability measures within the ML space.  Some custom measures on neural networks might be cool too.  Or something that includes the information theoretic ideas from the bottleneck principal paper.

Of course, I'll include ATE, CATE, ATT and few other causal inference conventions.
bungeegum 0.1.0
A set of tools to test payloads within the context of a standard Android application
Requires Python: >=3.8
Keywords: bungeegum, automation, android, testing, openstf, rce

# Bungeegum

Bungeegum is a set of tools designed to test code execution payloads within the
context of a standard Android application. By leveraging the powerful
[Frida](https://frida.re/docs/home/) instrumentation framework, it precisely
replicates the runtime conditions of an Android app, simulating the execution
of ELFs or shellcode as though they were triggered by a remote code execution
exploit.

## Prerequisites

- Python 3.8 or higher
- [Docker](https://docs.docker.com/get-docker/)

## Install

Note: Bungeegum is developed on and regularly tested with Ubuntu 18.04 and
Python 3.8. Other distributions and versions may work, but are currently
untested.

1. Clone the repository

2. Install dependencies

    ```sh
    sudo apt-get update
    sudo apt-get install python3-venv python3.8-venv make wget xz-utils -y
    python3.8 -m venv venv
    source venv/bin/activate
    (venv) pip install --upgrade pip
    ```

3. Build the APK and install the Python package by running the `make` command:

    ```sh
    make
    ```

## Supported Android Versions

Bungeegum has been tested successfully on Android 7, 9, 11, and 12.

## Usage

```sh
(venv) bungeegum -h
usage: bungeegum [-h] -d DEVICE [-r] (-s SHELLCODE | -e ELF) [-a [ARGS [ARGS ...]]]

Execute code within an application context

optional arguments:
  -h, --help            show this help message and exit
  -d DEVICE, --device DEVICE
                        ADB device ID to run on
  -r, --remote          Set if the file to be executed is on the device
  -s SHELLCODE, --shellcode SHELLCODE
                        Shellcode file to execute on the device
  -e ELF, --elf ELF     ELF file to execute on the device
  -a [ARGS [ARGS ...]], --args [ARGS [ARGS ...]]
                        Optional args to pass to the ELF file
```

## Examples

- Run an ELF from the host on the device:

```sh
bungeegum --elf ~/my_elf/arm64-v8a/my_elf
```

- Run a shellcode blob on the device

```sh
bungeegum --shellcode ~/my_shellcode.bin
```

- Run an on-device ELF:

```sh
bungeegum --remote --elf /system/bin/log --args "hello world"
```

## Contributing

Contributions are welcome! If you find any issues or have suggestions for
improvements, please open an issue or submit a pull request.

## License

This project is licensed under the GPLv2 License.