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This package provides tools for specifying and evaluating standard and truncated probability distributions, with support for log-space computation and joint distribution specification. It is designed to integrate seamlessl with the ‘ggdmc’ package, enabling Bayesian computation for cognition models. The package includes utilities for density calculation, sampling, and visualisation, making it easier to define priors and assess model behaviour in hierarchical Bayesian framework.
Although the package can be used independently, it is primarily intended to support the ‘ggdmc’ package, and is best used alongside other packages in the ‘ggdmc’ ecosystem.
cat("\n--------- Printing the joint prior distribution in C++-------\n")
p0 <- c(A = 0.15, B = 0.45, mean_v = 2.25, sd_v = 0.15, t0 = 0.2)
p1 <- rep(0.1, 5)
names(p1) <- names(p0)
p_prior <- BuildPrior(
p0 = p0,
p1 = p1,
lower = rep(NA, 5),
upper = rep(NA, 5),
log_p = rep(TRUE, 5),
dist = c("tnorm", "tnorm", "tnorm", "norm", "tnorm")
)
prior_S4 <- set_priors(p_prior = p_prior)
names(p_prior)
# Test print -----------------
res <- print_prior(p_prior)
# Test dprior -----------------
parameters <- seq(0.1, 0.5, 0.1)
res0 <- dprior(p_prior, parameters)
set.seed(123)
result1 <- rprior(p_prior, 1)
set.seed(123)
result2 <- rprior(p_prior, 2)
# Use the beta distribution to create uniform densities
# lower and upper set the bounds. If lower is NA, it will be set to 0.
# If upper is NA, it will be set to 1.
p_prior <- BuildPrior(
p0 = c(A = 1, B = 1, mean_v = 1, sd_v = 1, t0 = 1),
p1 = rep(1, 5),
lower = rep(0, 5),
upper = rep(5, 5),
dist = rep("beta", 5),
log_p = rep(TRUE, 5)
)
prior_S4 <- set_priors(p_prior = p_prior)
p0 <- plot_prior(p_prior)
The ‘ggdmcPrior’ package supports six standard statistical distributions—normal, truncated or regular normal, gamma, lognormal, Cauchy, and uniform—making it easy to define and evaluate priors for Bayesian cognitive modeling.
p0 <- c(A = 0.15, B = 0.45, mean_v = 2.25, sd_v = 0.15, t0 = 0.2)
tnorm_prior <- BuildPrior(
p0 = p0,
p1 = rep(1, 5),
lower = rep(0, 5),
upper = rep(NA, 5),
dist = rep("tnorm", 5),
log_p = rep(FALSE, 5)
)
gamma_prior <- BuildPrior(
p0 = p0,
p1 = rep(0.1, 5),
lower = rep(NA, 5),
upper = rep(NA, 5),
dist = rep("gamma", 5),
log_p = rep(FALSE, 5)
)
lnorm_prior <- BuildPrior(
p0 = p0,
p1 = rep(0.1, 5),
lower = rep(NA, 5),
upper = rep(NA, 5),
dist = rep("lnorm", 5),
log_p = rep(FALSE, 5)
)
cauchy_prior <- BuildPrior(
p0 = p0,
p1 = rep(0.1, 5),
lower = rep(NA, 5),
upper = rep(NA, 5),
dist = rep("cauchy", 5),
log_p = rep(FALSE, 5)
)
unif_prior <- BuildPrior(
p0 = p0,
p1 = rep(0.1, 5),
lower = rep(NA, 5),
upper = rep(NA, 5),
dist = rep("unif", 5),
log_p = rep(FALSE, 5)
)
norm_prior <- BuildPrior(
p0 = p0,
p1 = rep(0.1, 5),
lower = rep(NA, 5),
upper = rep(NA, 5),
dist = rep("norm", 5),
log_p = rep(FALSE, 5)
)
print(plot_prior(tnorm_prior, font_size = 2.5, cex = 2.5))
print(plot_prior(gamma_prior, font_size = 2.5, cex = 2.5))
print(plot_prior(lnorm_prior, font_size = 2.5, cex = 2.5))
print(plot_prior(cauchy_prior, font_size = 2.5, cex = 2.5))
print(plot_prior(unif_prior, font_size = 2.5, cex = 2.5))
print(plot_prior(norm_prior, font_size = 2.5, cex = 2.5))
nparameter <- length(p0)
for (i in seq_len(nparameter)) {
tnorm_prior[[i]]$log_p <- TRUE
gamma_prior[[i]]$log_p <- TRUE
lnorm_prior[[i]]$log_p <- TRUE
cauchy_prior[[i]]$log_p <- TRUE
unif_prior[[i]]$log_p <- TRUE
norm_prior[[i]]$log_p <- TRUE
}
res <- print_prior(tnorm_prior)
res <- print_prior(gamma_prior)
res <- print_prior(lnorm_prior)
res <- print_prior(cauchy_prior)
res <- print_prior(unif_prior)
res <- print_prior(norm_prior)
set.seed(123)
parameters_r <- runif(nparameter, 0, 10)
res0 <- sumlogprior(p_prior_r = tnorm_prior, parameters_r = parameters_r)
res1 <- sumlogprior(p_prior_r = gamma_prior, parameters_r = parameters_r)
res2 <- sumlogprior(p_prior_r = lnorm_prior, parameters_r = parameters_r)
res3 <- sumlogprior(p_prior_r = cauchy_prior, parameters_r = parameters_r)
res4 <- sumlogprior(p_prior_r = unif_prior, parameters_r = parameters_r)
res5 <- sumlogprior(p_prior_r = norm_prior, parameters_r = parameters_r)
results <- c(res0, res1, res2, res3, res4, res5)
R (>= 3.5.0), Rcpp (>= 1.0.7), RcppArmadillo (>= 0.10.7.5.0), ggdmcHeaders, lattice.
See DESCRIPTION for details
From CRAN:
install.packages("ggdmcPrior")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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