full_crossover: Perform a Full Crossover Step Using Differential Evolution

In dRiftDM: Estimating (Time-Dependent) Drift Diffusion Models

Perform a Full Crossover Step Using Differential Evolution

Description

This function updates each chain's parameters by proposing new values using a differential evolution strategy. For each chain k, two other chains m and n are randomly selected, and a proposal is generated via: prms_k + gamma * (prms_m - prms_n) + noise, where gamma = 2.38 / sqrt(2 * n_prms) and noise is uniform perturbation controlled by b. The proposal is accepted with Metropolis probability via call_log_posterior_m(), and accepted proposals replace the current values.

Usage

full_crossover(
  prms_across_chains,
  pis_across_chains,
  log_likes_across_chains,
  gamma = NULL,
  b = 0.001,
  ...
)

Arguments

prms_across_chains	a numeric matrix of dimension ⁠p × n⁠, where p is the number of parameters and n is the number of chains. Each column contains the current parameter vector of a chain.
pis_across_chains	a numeric vector of length n, containing the current log-posterior values for each chain.
log_likes_across_chains	a numeric vector of length n, containing the current log-likelihood values for each chain.
gamma	a single numeric tuning parameter, that scales the difference between parameters. If NULL, defaults to 2.38 / sqrt(2 * n_prms)
b	a small numeric value used to perturb the proposal parameters to avoid degeneracy.
...	additional arguments passed to call_log_posterior_m().

Value

A list with the following components:

• new_prms_across_chains: The updated parameter matrix of shape ⁠p × n⁠.

• new_pis_across_chains: The updated vector of log-posterior values.

• new_log_likes_across_chains: The updated vector of log-likelihood values.

dRiftDM documentation built on Dec. 1, 2025, 5:08 p.m.