semvec/pypi-packages huggingface dataset:
betabernsum 0.1.3
Sums of beta-bernoulli random variables

# betabernsum
Sums of beta-bernoulli variables

## Installation

For Python:
```sh
pip3 install betabernsum
```
or
```
pip3 install --user betabernsum
```

For R:
```r
library(devtools)
install_github("anthony-aylward/betabernsum")
```


## Examples

In Python:
```python
from betabernsum import bbs_pmf, bbs_cdf, bbs_test
help(bbs_pmf)
help(bbs_cdf)
help(bbs_test)
bbs_pmf(5, [30, 12], [4, 5], [6, 5], independent=False)
bbs_cdf(5, [30, 12], [4, 5], [6, 5], independent=False)
bbs_test(5, [30, 12], [4, 5], [6, 5], independent=False)
```

In R:
```r
library(betabernsum)
help(dbbs)
help(pbbs)
help(bbs_test)
dbbs(5, c(30, 12), shape1 = c(4, 5), shape2 = c(6, 5), independent = FALSE)
pbbs(5, c(30, 12), shape1 = c(4, 5), shape2 = c(6, 5), independent = FALSE)
bbs_test(5, c(30, 12), shape1 = c(4, 5), shape2 = c(6, 5), independent = FALSE)
```
bbprop 0.0.7
Hypothesis test for difference of beta-binomial proportions

# bbprop
This package provides a statistical test for the difference of beta-binomial proportions.

## Installation

```sh
pip3 install bbprop
```
or
```
pip3 install --user bbprop
```

## Hypothesis test details

The aim of the test is to determine whether two values drawn from beta-binomial distributions are significantly different from one another.

The null hypothesis of the test is that two random variables `B0` and `B1` have beta-binomial distribtions with parameters `theta0 = (n0, alpha0, beta0)` and `theta1 = (n1, alpha1, beta1)`, respectively.

The test statistic is the value `x = abs(b0/n0 - b1/n1)` where `b0` and `b1` are the number of successes drawn from `B0` and `B1`.

The p-value is then the probability that `x` is greater than or equal to its observed value, given the predefined null distribution.

## Examples

```python
from bbprop import bbprop_cdf, bbprop_test
help(bbprop_cdf)
help(bbprop_test)
bbprop_cdf(0.1, [30, 12], [4, 5], [6, 5])
bbprop_test(0.1, [30, 12], [4, 5], [6, 5])
```
bbpmf 0.0.3
Probability mass function for a beta-binomial distribution

# betabernsum
Sums of beta-bernoulli variables

## Installation

For Python:
```sh
pip3 install betabernsum
```
or
```
pip3 install --user betabernsum
```

For R:
```r
library(devtools)
install_github("anthony-aylward/betabernsum")
```


## Examples

In Python:
```python
from betabernsum import bbs_pmf, bbs_cdf, bbs_test
help(bbs_pmf)
help(bbs_cdf)
help(bbs_test)
bbs_pmf(5, [30, 12], [4, 5], [6, 5], independent=False)
bbs_cdf(5, [30, 12], [4, 5], [6, 5], independent=False)
bbs_test(5, [30, 12], [4, 5], [6, 5], independent=False)
```

In R:
```r
library(betabernsum)
help(dbbs)
help(pbbs)
help(bbs_test)
dbbs(5, c(30, 12), shape1 = c(4, 5), shape2 = c(6, 5), independent = FALSE)
pbbs(5, c(30, 12), shape1 = c(4, 5), shape2 = c(6, 5), independent = FALSE)
bbs_test(5, c(30, 12), shape1 = c(4, 5), shape2 = c(6, 5), independent = FALSE)
```
image-processing-package-csrdev2 0.1.1
Projeto do bootcamp da DIO, Desenvolvedora Karina Kato
Requires Python: >=3.8

# Image_processing

Description.
The package image_processing is udes to:
    Processing:
        - Histrogram matching
        - Structural similarity
        - Resize image
    Utils:
        - Read image
        - Save image
        - Plot image
        - Plot result
        - Plot histogram

## Installation<br><br>

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name<br>

```bash
pip install image-processing-package
```

## Author
Charlly Ribeiro

## License
[MIT](https://choosealicense.com/licenses/mit/)