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📦 lbaModel

CRAN Status Downloads License: GPL-3 R-CMD-check

🧠 Overview

lbaModel provides fast and flexible tools for working with the Linear Ballistic Accumulator (LBA) model, a widely used framework in cognitive psychology and neuroscience for simulating and analysing choice and response time (RT) data.

Key features:

While designed to be part of the ggdmc ecosystem, lbaModel is also fully functional as a standalone package.

📐 Conceptual Illustration

Here’s a schematic of the LBA model showing accumulators racing to threshold (similar to diffusion models, but with linear deterministic growth.)

Conceptual illustration of the LBA Model

Figure: The LBA model assumes evidence accumulates linearly and independently across choices, with start points, drawing from a uniform distribution and drift rates. The first accumulator to hit its threshold determines the response and response time.

🚀 Getting Started

Example: Minimal LBA Model

library(ggdmcModel)
library(ggdmcPrior)

model <- BuildModel(
  p_map = list(A = "1", B = "1", t0 = "1", mean_v = "M", sd_v = "1", st0 = "1"),
  match_map = list(M = list(s1 = "r1", s2 = "r2")),
  factors = list(S = c("s1", "s2")),
  constants = c(st0 = 0, sd_v = 1),
  accumulators = c("r1", "r2"),
  type = "lba"
)

# Set population-level prior
pop_mean <- c(A = 0.4, B = 0.5, mean_v.false = 0.15, mean_v.true = 2.5, t0 = 0.3)
pop_scale <- c(A = 0.1, B = 0.1, mean_v.false = 0.2, mean_v.true = 0.2, t0 = 0.05)

pop_dist <- BuildPrior(
  p0 = pop_mean,
  p1 = pop_scale,
  lower = c(0, 0, 0, 0, 0),
  upper = rep(NA, length(pop_mean)),
  dists = rep("tnorm", length(pop_mean)),
  log_p = rep(FALSE, length(pop_mean))
)

plot_prior(pop_dist)

Simulating Data

sub_model <- setLBA(model)
pop_model <- setLBA(model, population_distribution = pop_dist)

# One subject
p_vector <- c(A = 0.75, B = 1.25, mean_v.false = 1.5, mean_v.true = 2.5, t0 = 0.15)
dat <- simulate(sub_model, nsim = 256, parameter_vector = p_vector, n_subject = 1)

# Multiple subjects
hdat <- simulate(pop_model, nsim = 128, n_subject = 32)

📊 Visualising LBA Densities

# Parameters
params_tmp <- list(
  A = c(0.5, 0.5),
  b = c(1.0, 1.0),
  mean_v = c(2.0, 1.0),
  sd_v = c(1.0, 1.0),
  st0 = c(0.0, 0.0),
  t0 = c(0.2, 0.2)
)

# Convert to matrix
param_list2mat <- function(param_list) {
  n_row <- length(param_list[[1]])
  n_col <- length(param_list)
  out <- matrix(NA, nrow = n_row, ncol = n_col)
  for (i in seq_len(n_col)) out[, i] <- param_list[[i]]
  t(out)
}

params <- param_list2mat(params_tmp)
time_params <- c(0, 5, 0.01)
nv <- ncol(params)
is_pos <- rep(TRUE, nv)

pdfs <- theoretical_dlba(params, is_pos, time_params)
cdfs <- theoretical_plba(params, is_pos, time_params)

📦 Installation

install.packages("lbaModel")

From GitHub (Development Version)

⚠️ Requires development tools and extra dependencies.

# install.packages("devtools")
devtools::install_github("yxlin/lbaModel")

🔧 Dependencies

📚 Reference

If you use lbaModel, please cite:

Why lbaModel?

🤝 Contributing

Contributions are welcome! Please feel free to submit issues, fork the repo, or open pull requests.

📬 Contact

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.
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