README.Rmd

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output: github_document
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# `{Simulacron3}` <img src='man/figures/logo.png' style="float:right; height:200px;" align='right' />

The purpose of the `{Simulacron3}` package is to provide easy-to-use boilerplate
functionality for simple simulation studies. The most archetypal example
of a usecase is comparing the performance of multiple estimators as the
sample size of training data increases.

A fundamental thesis of this package is that many simulation studies (of the
statistical performance of estimators) follow the following workflow:

![A flow diagram with simulation (sim) in the middle, data generating process (dgp), estimators, and config (iterations, sample sizes, etc) leading into it, and out of the simulation coming summary statistics.](man/figures/simulation_diagram.png)

## Demonstration

```{r}
library(Simulacron3)
# the only thing Simulacron3 contains is the Simulation R6 Class, used below

# Example Usage
# Define a data generating process
dgp <- function(n) data.frame(x = rnorm(n), y = rnorm(n))

# Define some estimators 
estimators <- list(
  mean_estimator = function(data) mean(data$x),
  var_estimator = function(data) var(data$x)
)

# Define a summary statistics function 
# 
# An estimator can potentially return a lot more data than can be stored
# in one row of results, so the summary_stats functions are used to 
# condense that information down.  Here they're not doing very much, but
# in more advanced simulations we will see why they're crucial. 
summary_func <- function(iter = NULL, est_results, data = NULL) {
  data.frame(
    mean_est = est_results$mean_estimator,
    var_est = est_results$var_estimator
  )
}

# Create a simulation object
sim <- Simulation$new()

# Set up the simulation
sim$set_dgp(dgp)
sim$set_estimators(estimators)
sim$set_config(list(replications = 5000, sample_size = 500))
sim$set_summary_stats(summary_func)

# Run the simulation
sim$run()

# Retrieve results
results <- sim$get_results()
head(results)
```

See <https://ctesta01.github.io/Simulacron3/articles/Comparing-Estimators.html> for
a slightly more involved example.

## Parallelization 

Parallelization is supported, and as simple as passing `parallel = TRUE` 
to the `config` for your simulation and declaring a `plan(multisession)` 
with the `{future}` package. 

```{r}
#| cache: true
library(microbenchmark)

# let's benchmark the simulation we specified above 
microbenchmark::microbenchmark(sim$run(), times = 10)

# just change the config to run in parallel
sim$set_config(list(parallel = TRUE)) 
future::plan(future::multisession) # setup an appropriate future::plan 
microbenchmark::microbenchmark(sim$run(), times = 10)
```

Underlying this is usage of the `{future.apply}` package. See 
<https://future.futureverse.org/> for a description of the 
types of plans that can be specified.

## Package Internals 

`{Simulacron3}` is meant to have easy to understand source code (and
not too much of it) so that users can easily reason about what to expect
from their simulations. Check out the source, especially for the `$run()` method
here: 

  - <https://github.com/ctesta01/Simulacron3/blob/main/R/Simulation.R>

### Package Title Inspiration

To quote Wikipedia: 

<img src="https://upload.wikimedia.org/wikipedia/en/7/70/DanielFGalouye-Simulacron-3.jpg" align='left' hspace='15' width='100px' />

> Simulacron-3 (1964), by Daniel F. Galouye, is an American science fiction novel featuring an early literary description of a simulated reality. <br><br>
> ... As time and events unwind, [Fuller] progressively grasps that his own world is
probably not "real" and might be only a computer-generated simulation.

<br><br>

## Other Related Works

`{Simulacron3}` is one of many attempts to help with the workflow of running
simulations. A lot of inspiration was taken from: 

  - [simChef](https://github.com/Yu-Group/simChef)
  - [SimEngine](https://avi-kenny.github.io/SimEngine/)
  - [simcausal](https://www.jstatsoft.org/article/view/v081i02)
  - [simulator](https://github.com/jacobbien/simulator)