| Type: | Package |
| Title: | An MCMC Sampler Using the t-Walk Algorithm |
| Version: | 2.0.0 |
| Maintainer: | Rodrigo Fonseca Villa <rodrigo03.villa@gmail.com> |
| Description: | Implements the t-walk algorithm, a general-purpose, self-adjusting Markov Chain Monte Carlo (MCMC) sampler for continuous distributions as described by Christen & Fox (2010) <doi:10.1214/10-BA603>. The t-walk requires no tuning and is robust for a wide range of target distributions, including high-dimensional and multimodal problems. This implementation includes an option for running multiple chains in parallel to accelerate sampling and facilitate convergence diagnostics. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | parallel, stats, utils |
| Suggests: | mvtnorm, coda, devtools, roxygen2, knitr, rmarkdown, ellipse, ggplot2, ggthemes, gridExtra, reshape2, viridis |
| VignetteBuilder: | knitr |
| URL: | https://github.com/rodrigosqrt3/Rtwalk |
| BugReports: | https://github.com/rodrigosqrt3/Rtwalk/issues |
| NeedsCompilation: | no |
| Packaged: | 2026-01-28 23:38:33 UTC; rodri |
| Author: | Rodrigo Fonseca Villa [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2026-02-02 10:50:02 UTC |
Generate a proposal for the Blow kernel
Description
Generate a proposal for the Blow kernel
Usage
kernel_blow(n_dim, p_phi, x, xp)
Arguments
n_dim |
Dimension of the parameter space. |
p_phi |
Probability of updating each coordinate. |
x |
Current point 'x'. |
xp |
Current point ‘x’'. |
Value
A list with the 'proposal', 'n_phi', and 'phi'.
Generate a proposal for the Hop kernel
Description
Generate a proposal for the Hop kernel
Usage
kernel_hop(n_dim, p_phi, x, xp)
Arguments
n_dim |
Dimension of the parameter space. |
p_phi |
Probability of updating each coordinate. |
x |
Current point 'x'. |
xp |
Current point ‘x’'. |
Value
A list with the 'proposal', 'n_phi', and 'phi'.
Generate a proposal for the Traverse kernel
Description
Generate a proposal for the Traverse kernel
Usage
kernel_traverse(n_dim, p_phi, x, xp, beta)
Arguments
n_dim |
Dimension of the parameter space. |
p_phi |
Probability of updating each coordinate. |
x |
Current point 'x'. |
xp |
Current point ‘x’'. |
beta |
Step parameter beta. |
Value
A list with the 'proposal' and 'n_phi' (number of moved coordinates).
Generate a proposal for the Walk kernel
Description
Generate a proposal for the Walk kernel
Usage
kernel_walk(n_dim, p_phi, aw, x, xp)
Arguments
n_dim |
Dimension of the parameter space. |
p_phi |
Probability of updating each coordinate. |
aw |
Scale parameter 'a_w' (default 1.5). |
x |
Current point 'x'. |
xp |
Current point ‘x’'. |
Value
A list with the 'proposal' and 'n_phi'.
Calculate the log of the proposal density for the Blow kernel
Description
Calculate the log of the proposal density for the Blow kernel
Usage
log_density_blow(n_phi, phi, h, x, xp)
Arguments
n_phi |
Number of moved coordinates. |
phi |
Boolean vector indicating moved coordinates. |
h |
Proposed point. |
x |
Reference point. |
xp |
Reference point. |
Value
The value of -log(g(h)).
Calculate the log of the proposal density for the Hop kernel
Description
Calculate the log of the proposal density for the Hop kernel
Usage
log_density_hop(n_phi, phi, h, x, xp)
Arguments
n_phi |
Number of moved coordinates. |
phi |
Boolean vector indicating moved coordinates. |
h |
Proposed point. |
x |
Reference point. |
xp |
Reference point. |
Value
The value of -log(g(h)).
Simulate the beta parameter for the Traverse kernel
Description
Samples beta from the proposal distribution in Section 2.2 of Christen & Fox (2010).
Usage
simulate_beta(at = 6)
Arguments
at |
Shape parameter 'a_t' (default 6). |
Value
A numeric value for beta.
Run the t-walk MCMC Algorithm
Description
This function implements the t-walk algorithm by Christen & Fox (2010), a general-purpose MCMC sampler that does not require manual tuning. The function can run multiple independent MCMC chains in parallel to accelerate execution and facilitate convergence diagnostics.
Usage
twalk(log_posterior, n_iter, x0, xp0, n_chains = 1, n_cores = NULL, ...)
Arguments
log_posterior |
A function that takes a parameter vector as its first argument and returns the scalar log posterior density. Additional arguments can be passed to this function via '...'. |
n_iter |
The number of iterations to run for each chain. |
x0 |
A numeric vector with the initial values for the first point ('x'). |
xp0 |
A numeric vector with the initial values for the second point (‘x’'). |
n_chains |
The number of independent MCMC chains to run. Defaults to '1', which runs a single chain sequentially. If greater than 1, parallel mode is activated. |
n_cores |
The number of CPU cores to use in parallel mode. If 'NULL' (default), it will attempt to use all available cores minus one. |
... |
Additional arguments to be passed to the 'log_posterior' function. |
Value
A list containing:
all_samples |
A matrix with the combined samples from all chains. |
acceptance_rate |
The average acceptance rate across all chains. |
total_iterations |
The total number of samples generated (n_iter * n_chains). |
n_dim |
The dimension of the parameter space. |
individual_chains |
If 'n_chains > 1', a list containing the raw results from each separate chain, useful for diagnostics like R-hat. |
Examples
# Example 1: Sampling from a Bivariate Normal (sequential mode)
# The 'mvtnorm' package is required for this example
if (requireNamespace("mvtnorm", quietly = TRUE)) {
log_post <- function(x) {
mvtnorm::dmvnorm(x, mean = c(0, 0), sigma = matrix(c(1, 0.8, 0.8, 1), 2, 2), log = TRUE)
}
# Run with fewer iterations for a quick example
# Set a seed for reproducibility
set.seed(123)
result_seq <- twalk(log_posterior = log_post, n_iter = 5000,
x0 = c(-1, 1), xp0 = c(1, -1))
plot(result_seq$all_samples, pch = '.', main = "t-walk Samples (Sequential)")
}
# Example 2: The same problem in parallel (will run faster)
# Using 2 chains. n_iter is now per chain.
if (requireNamespace("mvtnorm", quietly = TRUE)) {
set.seed(123)
result_par <- twalk(log_posterior = log_post, n_iter = 2500,
x0 = c(-1, 1), xp0 = c(1, -1), n_chains = 2)
plot(result_par$all_samples, pch = '.', main = "t-walk Samples (Parallel)")
}
Run a single t-walk move (step)
Description
Selects one of the four kernels, generates a proposal, and calculates the Metropolis-Hastings acceptance probability.
Usage
twalk_move(
n_dim,
log_post_fun,
support_fun,
x,
U,
xp,
Up,
at = 6,
aw = 1.5,
p_phi = min(n_dim, 4)/n_dim,
p_traverse = 0.4918,
p_walk = 0.4918,
p_blow = 0.0082,
...
)
Arguments
... |
Arguments passed to 'log_post_fun' and 'support_fun'. |
Value
A list containing the proposal and the acceptance probability.