python-probabilities 0.4.3
Library for accurate statistical calculations using Python.
Requires Python: >=3.9,<4.0

# Python Probabilities 🐍
*Library for accurate statistical calculations using Python.*

- [Binomial Distributions](#binomial-distributions)
  - [Probability mass function](#probability-mass-function)
  - [Cumulative distribution function](#cumulative-distribution-function)
  - [Inverse cumulative distribution function](#inverse-cumulative-distribution-function)
- [Normal Distributions](#normal-distributions)
  - [Probability density function](#probability-density-function)
  - [Cumulative distribution function](#cumulative-distribution-function-1)
  - [Inverse cumulative distribution function](#inverse-cumulative-distribution-function-1)
- [Poisson Distributions](#poisson-distributions)
  - [Probability mass function](#probability-mass-function-1)
  - [Cumulative distribution function](#cumulative-distribution-function-2)
  - [Inverse cumulative distribution](#inverse-cumulative-distribution)
- [Geometric Distributions](#geometric-distributions)
  - [Probability mass function](#probability-mass-function-2)
  - [Cumulative distribution function](#cumulative-distribution-function-3)
  - [Inverse cumulative distribution function](#inverse-cumulative-distribution-function-2)

## Binomial Distributions
### Probability mass function
```python
BinomialPD(r, n, p)
```
For the random variable `X` with the binomial distribution `B(n, p)`, calculate the **probability mass function**.\
Where `r` is the number of successes, `n` is the number of trials, and `p` is the probability of success.

**Example**\
To calculate `P(X=7)` for the binomial distribution `X~B(11, 0.33)`:
```python
>>> from python_probabilities import BinomialPD
>>> BinomialPD(7, 11, 0.33)
0.029656979029412885
```
---
### Cumulative distribution function
```python
BinomialCD(r, n, p)
```
For the random variable `X` with the binomial distribution `B(n, p)`, calculate the **cumulative distribution function**.\
Where `r` is the number of successes, `n` is the number of trials, and `p` is the probability of success.

**Example**\
To calculate `P(X≤7)` for the binomial distribution `X~B(11, 0.33)`:
```python
>>> from python_probabilities import BinomialCD
>>> BinomialCD(7, 11, 0.33)
0.9912362670526581
```
---
### Inverse cumulative distribution function
```python
InvBinomialCD(q, n, p)
```
For the random variable `X` with the binomial distribution `B(n, p)`, calculate the **inverse** for the **cumulative distribution function**.\
Where `q` is the cumulative probability, `n` is the number of trials, and `p` is the probability of success.

`InvBinomialCD(q, n, p)` returns the smallest integer `x` such that `BinomialCD(x, n, p)` is greater than or equal to `q`.

**Example**\
To calculate the corresponding value for `r` (the number of successes) given the value for `q` (the cumulative probability):
```python
>>> from python_probabilities import BinomialCD, InvBinomialCD
>>> InvBinomialCD(0.9912362670526581, 11, 0.333)
7
>>> BinomialCD(7, 11, 0.333)
0.9912362670526581
```

## Normal Distributions
### Probability density function
```python
NormalPD(x, µ, σ)
```
Probability density function for the normal distribution `X~N(µ, σ)`.\
Where `µ` is the mean, and `σ` is the standard deviation.

---

### Cumulative distribution function
```python
NormalCD(x, µ, σ)
```
Cumulative distribution function for the normal distribution `X~N(µ, σ)`.\
Where `µ` is the mean, and `σ` is the standard deviation.

**Example**\
To calculate `P(X≤0.891)` for the normal distribution `X~N(0.734, 0.114)`:
```python
>>> from python_probabilities import NormalCD
>>> NormalCD(0.891, 0.734, 0.114)
0.9157737045522477
```

---

### Inverse cumulative distribution function
```python
InvNormalCD(y, µ, σ)
```
Inverse cumulative distribution function for the normal distribution `X~N(µ, σ)`.\
Where `µ` is the mean, and `σ` is the standard deviation.

`InvNormalCD(y, µ, σ)` returns the smallest integer `x` such that `NormalCD(x, µ, σ)` is greater than or equal to `y`.

**Example**\
To calculate the corresponding value for `x` given the value for `y`:
```python
>>> from python_probabilities import NormalCD, InvNormalCD
>>> InvNormalCD(0.9157737045522477, 0.734, 0.114)
0.891
>>> NormalCD(0.891, 0.734, 0.114)
0.9157737045522477
```

## Poisson Distributions
### Probability mass function
```python
PoissonPD(r, m)
```
For the random variable `X` with the poisson distribution `Po(m)`, calculate the **probability mass function**.\
Where `r` is the number of occurrences, and `m` is the mean rate of occurrence.

**Example**\
To calculate `P(X=7)` for the poisson distribution `X~Po(11.556)`:
```python
>>> from python_probabilities import PoissonPD
>>> PoissonPD(11, 23.445)
0.0019380401123575617
```
---
### Cumulative distribution function
```python
PoissonCD(r, m)
```
For the random variable `X` with the poisson distribution `Po(m)`, calculate the **cumulative distribution function**.\
Where `r` is the number of occurrences, and `m` is the mean rate of occurrence.

**Example**\
To calculate `P(X≤7)` for the poisson distribution `X~Po(11.556)`:
```python
>>> from python_probabilities import PoissonCD
>>> PoissonCD(11, 23.445)
0.0034549033698374467
```
---
### Inverse cumulative distribution
```python
InvPoissonCD(q, m)
```
For the random variable `X` with the poisson distribution `Po(m)`, calculate the **inverse** for the **cumulative distribution function**.\
Where `q` is the cumulative probability, and `m` is the mean rate of occurrence.

`InvPoissonCD(q, m)` returns the smallest integer `x` such that `PoissonCD(x, m)` is greater than or equal to `q`.

**Example**\
To calculate the corresponding value for `r` (number of occurrences) given the values for `q` (cumulative probability):
```python
>>> from python_probabilities import PoissonCD, InvPoissonCD
>>> InvPoissonCD(0.0034549033698374467, 23.445)
11
>>> PoissonCD(11, 23.445)
0.0034549033698374467
```

## Geometric Distributions
### Probability mass function
```python
GeometricPD(x, p)
```
Probability mass function for the geometric distribution `X~G(p)`.\
Where `x` is the number of trials before the first success, and `p` is the probability of success.

**Example**\
To calculate `P(X=3)` for the geometric distribution `X~G(0.491)`:
```python
>>> from python_probabilities import GeometricPD
>>> GeometricPD(3, 0.491)
0.127208771
```
---

### Cumulative distribution function
```python
GeometricCD(x, p)
```
Cumulative distribution function for the geometric distribution `X~G(p)`.\
Where `x` is the number of trials before the first success, and `p` is the probability of success.

**Example**\
To calculate `P(X≤3)` for the geometric distribution `X~G(0.491)`:
```python
>>> from python_probabilities import GeometricCD
>>> GeometricCD(3, 0.491)
0.868127771
```
---

### Inverse cumulative distribution function
```python
InvGeometricCD(area, p)
```
Inverse cumulative distribution function for the geometric distribution `X~G(p)`.\
Where `x` is the number of trials before the first success, and `p` is the probability of success.

`InvGeometricCD(area, p)` returns the smallest integer `x` such that `GeometricCD(x, p)` is greater than or equal to `area`.

**Example**\
To calculate the corresponding value for `x` given the value for `area`:
```python
>>> from python_probabilities import GeometricCD, InvGeometricCD
>>> InvGeometricCD(0.868, 0.491)
3
>> GeometricCD(3, 0.491)
0.868127771
```