semvec/pypi-packages huggingface dataset:
tweetple 0.93
A python wrapper for users of the Academic Research product track.

# TWEETPLE

[![Twitter API v2 badge](https://img.shields.io/endpoint?url=https%3A%2F%2Ftwbadges.glitch.me%2Fbadges%2Fv2)](https://developer.twitter.com/en/docs/twitter-api/early-access)

## Installation

The easiest way to install the latest version from PyPI is by using pip:
```python
pip install tweetple
```

## Usage

+ Users, Followers, Liking Users and Retweeted By Lookup

```python
import tweetple

from tweetple import TweetPle

# Bearer token accesible via Twitter Developer Academic Research Track
bearer_token='AAAAAAAA'

# List of handle ids
ids = ['308131814']

# Retrieve users' information
TweetPle.TweepleStreamer(ids, bearer_token).user_lookup()

# Retrieve followers' information
TweetPle.TweepleStreamer(ids, bearer_token).followers_lookup()

# List of tweet ids

ids = ['308131814']

# Retrieve liking users
TweetPle.TweepleStreamer(ids, bearer_token).likes_lookup()

# Retrieve retweeting users

TweetPle.TweepleStreamer(ids, bearer_token).retweet_lookup()
```

+ Retrieve Tweets

One can provide as input a **list** of:

1. Tweets' ids

2. Tweeples' handles

3. Links

```python
import tweetple

from tweetple import TweetPle

# bearer token accesible via Twitter Developer Academic Research Track
bearer_token='AAAAAAAA'

# list of tweets' ids
tweetl = ['1461090445702881281']
TweetPle.TweetStreamer(tweetl, bearer_token).main()

# list of tweeplers' handles
tweeplel = ['zorroyanez']
TweetPle.TweetStreamer(tweeplel, bearer_token).main()

# list of links potentially shared via twitter
linkl = ['https://lula.com.br/22-vitorias-judiciais-de-lula-inquerito-contra-filhos-e-encerrado-por-falta-de-provas/']
TweetPle.TweetStreamer(linkl, bearer_token).main()

```
autodiff30-Sam-Jason-Elie-Raphael-Lea 0.0.2
Implementation of Auto-differentiation within the Harvard University AC207 Systems Development graduate course.
Requires Python: >=3.7

# team30
![test](https://code.harvard.edu/CS107/team30/actions/workflows/test.yml/badge.svg)
![coverage](https://code.harvard.edu/CS107/team30/actions/workflows/coverage.yml/badge.svg)

Project repo
betaMixPy 0.1.4
A Python package to find strong correlations among P variables, each with N observations.

# betaMixPy
The betaMixPy package is used to find edges in gene networks using co-expression data. The betaMixPy method is built on the insightful results of Frankl and Maehara (1990) who showed that two random vectors are approximately perpendicular with high probability, if the dimension of the space is sufficiently large. The pair-wise correlations between pairs of predictors are equal to the cosine of the angles between the pairs. From these angles we compute $z_j=\sin^2(\theta)$ and fit a mixture of two beta distributions to the $z_j$'s. For pairs of random vectors (the null set) the distribution of $z_j$ is Beta((N-1)/2, 1/2), where N is the sample size. The nonnull set is assumed to follow a Beta(a,b) distribution, and using the EM algorithm we estimate a,b, and the proportion, p0, of the null set of pairs. The betaMix function determines a threshold which will control the error rate given by the user. Any $z_j$ below that threshold corresponds to a significantly correlated pair of predictors (an edge in the graphical model.)

The input to the program is a normalized expression matrix, with predictors/genes (nodes) in the rows, and samples in the columns.

With a large number of predictors, P, the estimation may be slow, so it is recommended to set the parameter subsamplesize to something smaller than choose(P,2). The minimum allowed by the program is 20,000. Using anything smaller will cause betaMixPy to fit the model to all choose(P,2) pairs. 

If the N samples can be assumed to be indepndent, set the parameter ind to True (the default). If it is set to False, the null set follows a Beta((nu-1)/2, 1/2) distribution and nu (the effective sample size) is estimated from the data in the EM algorithm.

To install the package, run
```
pip3 install betaMixPy
```

Additional documentation and examples are available in the doc, code and data folders on [GitHub](https://github.com/haimbar/betaMixPy)