The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

overture

Features

Overture makes writing Markov chain Monte Carlo (MCMC) samplers simpler. With overture you can:

• Write less code Overture eliminates boilerplate code, looping through sampling functions and saving the results automatically.
• Easily recover from interruptions Samples can be saved on-disk as the MCMC runs, so it’s easy to resume the chain if something goes wrong.
• Run more chains in parallel Saving samples on-disk results in a dramatically smaller memory footprint for high-dimensional models, allowing more chains to be run when available RAM is limited.
• Monitor chain progress Samples can be viewed in another R process while the MCMC is still running.

Usage

Basic Usage

Using overture is easy:

1. Write the sampling functions

SampleX <- function(x) {
    x + 1
}

SampleY <- function(y) {
    y * y
}

2. Initialize the MCMC

Mcmc <- InitMcmc(3) # Run the chain for 3 iterations

3. Set initial values for the chain

x <- c(0, 10) # Initial value for x
y <- 2 # Initial value for y

4. Run the MCMC

samples <- Mcmc({
    x <- SampleX(x)
    y <- SampleY(y)
})

5. Analyze the results

> samples$x
     [,1] [,2]
[1,]    1   11
[2,]    2   12
[3,]    3   13
> samples$y
     [,1]
[1,]    4
[2,]   16
[3,]  256

Save samples on-disk

To save samples on disk, specify the directory where the samples should be saved:

Mcmc <- InitMcmc(3, backing.path="/save/directory/path/")
samples <- Mcmc({
    x <- SampleX(x)
    y <- SampleY(y)
})

The samples can be analyzed as before:

> samples$x[,]
     [,1] [,2]
[1,]    1   11
[2,]    2   12
[3,]    3   13
> samples$y[,, drop=FALSE]
     [,1]
[1,]    4
[2,]   16
[3,]  256

To load the samples from disk, use LoadMcmc:

loaded.samples <- LoadMcmc("/save/directory/path/")

To convert a file-backed MCMC into a list of R in-memory matrices, use ToMemory:

samples.in.memory <- ToMemory(loaded.samples)
> samples.in.memory
$x
     [,1] [,2]
[1,]    1   11
[2,]    2   12
[3,]    3   13

$y
     [,1]
[1,]    4
[2,]   16
[3,]  256

Monitor the progress of an MCMC while it’s still running

Samples from an MCMC can be viewed before its completion. First, start the slow running MCMC as a file-backed chain:

SlowMcmc <- InitMcmc(10000, backing.path="/save/directory/path/")
SlowMcmc({
    x <- SlowSampler()
})

Then, in another R process while the MCMC is still running, use Peek to load and analyze the samples taken so far:

samples.so.far <- Peek("/save/directory/path/")
samples.so.far$x[,]

Get more information

More examples and details are given in the package documentation.

Installation

To install from CRAN run:

install.packages("overture")

To install from GitHub, after installing devtools run:

devtools::install_github("kurtis-s/overture")

If you aren’t sure which version to install, you probably want to install from CRAN.

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.