Running examples in ‘pcvr-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: conjugate
> ### Title: Bayesian testing using conjugate priors and method of moments
> ###   for single or multi value traits.
> ### Aliases: conjugate
> ### Keywords: ROPE bayesian conjugate priors
> 
> ### ** Examples
> 
> mv_ln <- mvSim(
+   dists = list(
+     rlnorm = list(meanlog = log(130), sdlog = log(1.2)),
+     rlnorm = list(meanlog = log(100), sdlog = log(1.3))
+   ),
+   n_samples = 30
+ )
> 
> # lognormal mv
> ln_mv_ex <- conjugate(
+   s1 = mv_ln[1:30, -1], s2 = mv_ln[31:60, -1], method = "lognormal",
+   priors = list(mu = 5, sd = 2),
+   plot = FALSE, rope_range = c(-40, 40), rope_ci = 0.89,
+   cred.int.level = 0.89, hypothesis = "equal", support = NULL
+ )
> 
> # lognormal sv
> ln_sv_ex <- conjugate(
+   s1 = rlnorm(100, log(130), log(1.3)), s2 = rlnorm(100, log(100), log(1.6)),
+   method = "lognormal",
+   priors = list(mu = 5, sd = 2),
+   plot = FALSE, rope_range = NULL, rope_ci = 0.89,
+   cred.int.level = 0.89, hypothesis = "equal", support = NULL
+ )
> 
> # Z test mv example
> 
> mv_gauss <- mvSim(
+   dists = list(
+     rnorm = list(mean = 50, sd = 10),
+     rnorm = list(mean = 60, sd = 12)
+   ),
+   n_samples = 30
+ )
> 
> gauss_mv_ex <- conjugate(
+   s1 = mv_gauss[1:30, -1], s2 = mv_gauss[31:60, -1], method = "gaussian",
+   priors = list(mu = 30, n = 1, s2 = 100),
+   plot = FALSE, rope_range = c(-25, 25), rope_ci = 0.89,
+   cred.int.level = 0.89, hypothesis = "equal", support = NULL
+ )
> 
> # T test sv example
> 
> gaussianMeans_sv_ex <- conjugate(
+   s1 = rnorm(10, 50, 10), s2 = rnorm(10, 60, 12), method = "t",
+   priors = list(mu = 30, n = 1, s2 = 100),
+   plot = FALSE, rope_range = c(-5, 8), rope_ci = 0.89,
+   cred.int.level = 0.89, hypothesis = "equal", support = NULL
+ )
> 
> # beta mv example
> 
> set.seed(123)
> mv_beta <- mvSim(
+   dists = list(
+     rbeta = list(shape1 = 5, shape2 = 8),
+     rbeta = list(shape1 = 10, shape2 = 10)
+   ),
+   n_samples = c(30, 20)
+ )
> 
> beta_mv_ex <- conjugate(
+   s1 = mv_beta[1:30, -1], s2 = mv_beta[31:50, -1], method = "beta",
+   priors = list(a = 0.5, b = 0.5),
+   plot = FALSE, rope_range = c(-0.1, 0.1), rope_ci = 0.89,
+   cred.int.level = 0.89, hypothesis = "equal"
+ )
> 
> # beta sv example
> 
> beta_sv_ex <- conjugate(
+   s1 = rbeta(20, 5, 5), s2 = rbeta(20, 8, 5), method = "beta",
+   priors = list(a = 0.5, b = 0.5),
+   plot = FALSE, rope_range = c(-0.1, 0.1), rope_ci = 0.89,
+   cred.int.level = 0.89, hypothesis = "equal"
+ )
> 
> # binomial sv example
> # note that specifying trials = 20 would also work
> # and the number of trials will be recycled to the length of successes
> 
> binomial_sv_ex <- conjugate(
+   s1 = list(successes = c(15, 14, 16, 11), trials = c(20, 20, 20, 20)),
+   s2 = list(successes = c(7, 8, 10, 5), trials = c(20, 20, 20, 20)), method = "binomial",
+   priors = list(a = 0.5, b = 0.5),
+   plot = FALSE, rope_range = c(-0.1, 0.1), rope_ci = 0.89,
+   cred.int.level = 0.89, hypothesis = "equal"
+ )
> 
> # poisson sv example
> 
> poisson_sv_ex <- conjugate(
+   s1 = rpois(20, 10), s2 = rpois(20, 8), method = "poisson",
+   priors = list(a = 0.5, b = 0.5),
+   plot = FALSE, rope_range = c(-1, 1), rope_ci = 0.89,
+   cred.int.level = 0.89, hypothesis = "equal"
+ )
> 
> # negative binomial sv example
> # knowing r (required number of successes) is an important caveat for this method.
> # in the current implementation we suggest using the poisson method for data such as leaf counts
> 
> negbin_sv_ex <- conjugate(
+   s1 = rnbinom(20, 10, 0.5), s2 = rnbinom(20, 10, 0.25), method = "negbin",
+   priors = list(r = 10, a = 0.5, b = 0.5),
+   plot = FALSE, rope_range = c(-1, 1), rope_ci = 0.89,
+   cred.int.level = 0.89, hypothesis = "equal"
+ )
> 
> # von mises mv example
> 
> mv_gauss <- mvSim(
+   dists = list(
+     rnorm = list(mean = 50, sd = 10),
+     rnorm = list(mean = 60, sd = 12)
+   ),
+   n_samples = c(30, 40)
+ )
> vm1_ex <- conjugate(
+   s1 = mv_gauss[1:30, -1],
+   s2 = mv_gauss[31:70, -1],
+   method = "vonmises",
+   priors = list(mu = 45, kappa = 1, boundary = c(0, 180), known_kappa = 1, n = 1),
+   plot = FALSE, rope_range = c(-1, 1), rope_ci = 0.89,
+   cred.int.level = 0.89, hypothesis = "equal"
+ )
Error in loadNamespace(x) : there is no package called ‘brms’
Calls: conjugate ... loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart
Execution halted