The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
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[@]metanet.ch.

overture

Build Status Coverage status CRAN status

Features

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

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.