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Stochastic Process Simulation Engine for R
A modular, research-grade simulator for stochastic processes with variance reduction, parallel execution, and rich visualization.
# 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")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.
| 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 |
MIT
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.