semvec/pypi-packages huggingface dataset:
BrainParc 0.0.1
Brain Parcellation Tools

# Repo for Brain Parcellation Tools
```shell
Copyright IDEA Lab, School of Biomedical Engineering, ShanghaiTech. Shanghai, China

Licensed under the the GPL (General Public License);
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Repo for Brain Parcellation Tools (BrainParc)
Contact: JiamengLiu.PRC@gmail.com
```
AutoParc 0.0.1
Automatic Brain Parcellation Tools

# Repo for Automatic Brain Parcellation Tools
```shell
Copyright IDEA Lab, School of Biomedical Engineering, ShanghaiTech. Shanghai, China

Licensed under the the GPL (General Public License);
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Repo for Automatic Brain Extraction Tools (AutoBET)
Contact: JiamengLiu.PRC@gmail.com
```
pyfbsdgpio-tspspi 0.0.1
FreeBSD gpio device ioctl wrapper
Requires Python: >=3.6

# FreeBSD GPIO ioctl wrapper for Python

This is a simple wrapper around the FreeBSD ```ioctl``` calles for the
GPIO device. It allows very simple access to configuration and I/O facilities
of the GPIO pins.

## Installation

```
pip install pyfbsdgpio-tspspi
```

## Basic usage

For now this section only summarizes the most basic usage of the module (it
also supports setting pullup and pulldown resistors and selecting different
drive characteristics if the GPIO chipset supports it).

### Determining pin count of the GPIO module

One can determine the number of I/O pins available on the GPIO device:

```
from labdevices.gpio import GpioDirection, GpioDrive, GpioPull
from fbsdgpio import FbsdGPIO

gpio = FbsdGPIO()
print(f"We have {gpio.getIOCount()} I/O ports")
```

### Fetching the current configuration of an I/O port

```
from labdevices.gpio import GpioDirection, GpioDrive, GpioPull
from fbsdgpio import FbsdGPIO

gpio = FbsdGPIO()
print(f"Current configuration of pin 13: {gpio.getConfig(13)}")
```

### Setting pin as input or output

```
from labdevices.gpio import GpioDirection, GpioDrive, GpioPull
from fbsdgpio import FbsdGPIO

with FbsdGPIO() as gpio:
    gpio.setConfig(13, direction=GpioDirection.Input)
```

```
with FbsdGPIO() as gpio:
    gpio.setConfig(13, direction=GpioDirection.Output)
```

### Pulse output

```
# Pulse for 100 microseconds

with FbsdGPIO() as gpio:
    gpio.pulse(13, 100)
```

Also specifying the pulsed (not current) state so the module
does not have to query it:

```
with FbsdGPIO() as gpio:
    gpio.pulse(13, 100, False)
```
Debias-Infer 0.0.8
Efficient Inference on High-Dimensional Linear Models With Missing Outcomes
Requires Python: >=3.8

[![PyPI pyversions](https://img.shields.io/pypi/pyversions/Debias-Infer.svg)](https://pypi.python.org/pypi/Debias-Infer/)
[![PyPI version](https://badge.fury.io/py/Debias-Infer.svg)](https://badge.fury.io/py/Debias-Infer)
[![Downloads](https://static.pepy.tech/badge/Debias-Infer)](https://pepy.tech/project/Debias-Infer)
[![Documentation Status](https://readthedocs.org/projects/sconce-scms/badge/?version=latest)](http://debias-infer.readthedocs.io/?badge=latest)

# Efficient Inference on High-Dimensional Linear Models With Missing Outcomes

This package implements the proposed debiasing method for conducting valid inference on the high-dimensional linear regression function with missing outcomes. We also document all the code for the simulations and real-world applications in our paper [here](https://github.com/zhangyk8/Debias-Infer/tree/main/Paper_Code).

* Free software: MIT license
* Python Package Documentation: [https://debias-infer.readthedocs.io](https://debias-infer.readthedocs.io).
* You may also consider using our R package [DebiasInfer](https://cran.r-project.org/web/packages/DebiasInfer/index.html), though the Python package will be computationally faster.

Installation guide
--------

```Debias-Infer``` requires Python 3.8+ (earlier version might be applicable), [NumPy](http://www.numpy.org/), [SciPy](https://www.scipy.org/), [scikit-learn](https://scikit-learn.org/stable/), [CVXPY](https://www.cvxpy.org/), [statsmodels](https://www.statsmodels.org/). To install the latest version of ```Debias-Infer``` from this repository, run:

```
python setup.py install
```

To pip install a stable release, run:
```
pip install Debias-Infer
```

References
--------

<a name="debias">[1]</a> Y. Zhang, A. Giessing, Y.-C. Chen (2023+) Efficient Inference on High-Dimensional Linear Models with Missing Outcomes [arXiv:2309.06429](https://arxiv.org/abs/2309.06429).

<a name="scaledlasso">[2]</a> T. Sun and C.-H. Zhang (2012). Scaled Sparse Linear Regression. *Biometrika*, **99**, no.4: 879-898.