```
ts_brownian_motion(
.time = 100,
.num_sims = 10,
.delta_time = 1,
.initial_value = 0
)
```

# Introduction

Hello R users!

I am excited to announce a new update to the `{healthyR.ts}`

package: the `ts_brownian_motion()`

function.

This function allows you to easily simulate brownian motion, also known as a Wiener process, using just a few parameters. You can specify the length of the simulation using the ‘.time’ parameter, the number of simulations to run using the ‘.num_sims’ parameter, the time step size (standard deviation) using the ‘.delta_time’ parameter, and the initial value (which is set to 0 by default) using the ‘.initial_value’ parameter.

But what is brownian motion, and why might you want to simulate it? Brownian motion is a random process that describes the movement of particles suspended in a fluid. It is named after the botanist Robert Brown, who observed the random movement of pollen grains suspended in water under a microscope in the 19th century.

In finance, brownian motion is often used to model the movement of stock prices over time. By simulating brownian motion, you can get a sense of how prices might fluctuate in the future, and use this information to inform your investment decisions.

I hope that the `ts_brownian_motion()`

function will be a useful tool for anyone interested in simulating brownian motion, whether for financial modeling or any other application. Give it a try and see what you can do with it!

Right now the function is a bit slow at `.num_sims`

> 500 so I am working on optimizing it. I will also later on be introducing the Geometric Brownian Motion to `{healthyR.ts}`

As always, we welcome feedback and suggestions for new features and improvements. Thank you for using the {healthyR.ts} package, and happy simulating!

# Function

Here is the full function call:

# Example

A simple example of the output.

```
library(healthyR.ts)
ts_brownian_motion()
```

```
# A tibble: 1,010 × 3
sim_number t y
<fct> <dbl> <dbl>
1 1 0 0
2 1 1 1.46
3 1 2 2.68
4 1 3 2.78
5 1 4 3.07
6 1 5 3.43
7 1 6 3.05
8 1 7 4.43
9 1 8 6.04
10 1 9 6.89
# … with 1,000 more rows
```

Voila!