# StochSimR

**Stochastic Process Simulation Engine for R**

A modular, research-grade simulator for stochastic processes with variance
reduction, parallel execution, and rich visualization.

## Installation

```r
# From source tarball
install.packages("StochSimR_1.0.0.tar.gz", repos = NULL, type = "source")

# Or from local directory
devtools::install_local("path/to/StochSimR")
```

## Quick Start

```r
library(StochSimR)

# Simulate and visualise Brownian motion
paths <- sim_brownian(T_max = 1, n_steps = 1000, n_paths = 100)
plot_paths(paths, show_mean = TRUE, show_bands = TRUE)

# Stock price model (GBM)
stock <- sim_gbm(T_max = 1, n_steps = 252, mu = 0.08, sigma = 0.25,
                 x0 = 100, n_paths = 50)
plot_paths(stock)
plot_distribution(stock)
path_summary(stock)
```

See `vignette("introduction", package = "StochSimR")` for the full tutorial.

## Available Processes

| Process | Function | Methods |
|---|---|---|
| Poisson | `sim_poisson()` | exact, thinning |
| Brownian Motion | `sim_brownian()` | exact, bridge |
| Markov Chain | `sim_markov()` | exact |
| Geometric Brownian Motion | `sim_gbm()` | exact, euler |
| Ornstein-Uhlenbeck | `sim_ou()` | exact, euler |
| Levy Processes | `sim_levy()` | stable, gamma, NIG, variance-gamma |
| Jump-Diffusion | `sim_jump_diffusion()` | euler |
| Hawkes Process | `sim_hawkes()` | ogata thinning |

## License

MIT
