Running examples in ‘bayes4psy-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plot_trace,ttest_class-method
> ### Title: plot_trace
> ### Aliases: plot_trace,ttest_class-method plot_trace_ttest
> 
> ### ** Examples
> 
> # to use the function you first have to prepare the data and fit the model
> # see class documentation for an example of the whole process
> # along with an example of how to use this function
> ?ttest_class
ttest_class-class          package:bayes4psy           R Documentation

_t_t_e_s_t__c_l_a_s_s

_D_e_s_c_r_i_p_t_i_o_n:

     An S4 class for storing results of Bayesian t-test results.

     *Functions*

     summary(`ttest_class`): prints a summary of the fit.

     print(`ttest_class`): prints a more detailed summary of the fit

     show(`ttest_class`): prints a more detailed summary of the fit.

     plot(`ttest_class`): plots fitted model against the data. Use this
     function to explore the quality of your fit.

     plot_fit(`ttest_class`): plots fitted model against the data. Use
     this function to explore the quality of your fit.

     plot_trace(`ttest_class`): traceplot for main fitted model
     parameters.

     get_parameters(`ttest_class`): returns a dataframe with values of
     fitted parameters.

     compare_means(`ttest_class`, fit2=`ttest_class`): prints
     difference/equality of the first group against the second group.
     You can also provide the rope parameter or execute the comparison
     through the sigma or nu parameter.

     compare_means(`ttest_class`, mu=`numeric`): prints
     difference/equality of the first group against a mean value. You
     can also provide the rope parameter or execute the comparison
     through the sigma parameter.

     compare_means(`ttest_class`, mu=`numeric`, sigma=`numeric`):
     prints difference/equality of the first group against a normal
     distribution provided with mean value and standard deviation. Note
     here that sigma is used only in the Cohen's d calculation. You can
     also provide the rope parameter or execute the comparison through
     the sigma or nu parameter.

     compare_means(`ttest_class`, fits=`list`): prints
     difference/equality of the first group and multiple other groups.
     You can also provide the rope parameter or execute the comparison
     through the sigma or nu parameter.

     plot_means_difference(`ttest_class`, fit2=`ttest_class`): a
     visualization of the difference between the first group and the
     second group. You can also provide the rope and bins (number of
     bins in the histogram) parameters or visualize the comparison
     through the sigma or nu parameter.

     plot_means_difference(`ttest_class`, mu=`numeric`): a
     visualization of the difference between the first group and a
     constant value or a normal distribution with mean value mu. You
     can also provide the rope and bins (number of bins in the
     histogram) parameters or visualize the comparison through the
     sigma or nu parameter.

     plot_means_difference(`ttest_class`, fits=`list`): a visualization
     of the difference between multiple groups. You can also provide
     the rope and bins (number of bins in the histogram) parameters or
     visualize the comparison through the sigma or nu parameter.

     plot_means(`ttest_class`): plots density of means. You can also
     visualize the density for the sigma or nu parameter.

     plot_means(`ttest_class`, fit2=`ttest_class`): plots density for
     the first and the second group means. You can also visualize the
     density for the sigma or nu parameter.

     plot_means(`ttest_class`, mu=`numeric`): plots density for the
     first group means and a mean value in case second group is defined
     as a normal distribution or as a constant. You can also visualize
     the density for the sigma or nu parameter.

     plot_means(`ttest_class`, fits=`list`): plots density for the
     first group means and means for multiple other groups. You can
     also visualize the density for the sigma or nu parameter.

     compare_distributions(`ttest_class`, fit2=`ttest_class`): draws
     samples from distribution of the first group and compares them
     against samples drawn from the distribution of the second group.
     You can also provide the rope parameter.

     compare_distributions(`ttest_class`, mu=`numeric`): draws samples
     from distribution of the first group and compares them against a
     mean value. You can also provide the rope parameter.

     compare_distributions(`ttest_class`, mu=`numeric`,
     sigma=`numeric`): draws samples from distribution of the first
     group and compares them against samples from a normal distribution
     with a defined mean value and variance. You can also provide the
     rope parameter.

     compare_distributions(`ttest_class`, fits=`list`): draws samples
     from distribution of the first group and compares them against
     samples drawn from multiple other groups. You can also provide the
     rope parameter.

     plot_distributions(`ttest_class`): a visualization of the fitted
     distribution.

     plot_distributions(`ttest_class`, fit2=`ttest_class`): a
     visualization of two fitted distributions.

     plot_distributions(`ttest_class`, mu=`numeric`): a visualization
     of the fitted distribution and a constant value.

     plot_distributions(`ttest_class`, mu=`numeric`, sigma=`numeric`):
     a visualization of the fitted distribution and the normal
     distribution defined with a mean value and a standard deviation.

     plot_distributions(`ttest_class`, fits=`list`): a visualization of
     multiple fitted distributions.

     plot_distributions_difference(`ttest_class`, fit2=`ttest_class`):
     a visualization of the difference between the distribution of the
     first group and the distribution of the second group. You can also
     provide the rope and bins (number of bins in the histogram)
     parameters.

     plot_distributions_difference(`ttest_class`, mu=`numeric`): a
     visualization of the difference between the distribution of the
     first group and a constant value. You can also provide the rope
     and bins (number of bins in the histogram) parameters.

     plot_distributions_difference(`ttest_class`, mu=`numeric`,
     sigma=`numeric`): a visualization of the difference between the
     distribution of the first group and the normal distribution
     defined with a mean value and standard deviation. You can also
     provide the rope and bins (number of bins in the histogram)
     parameters.

     plot_distributions_difference(`ttest_class`, fits=`list`): a
     visualization of the difference between multiple groups. You can
     also provide the rope and bins (number of bins in the histogram)
     parameters.

_S_l_o_t_s:

     ‘extract’ Extract from Stan fit.

     ‘fit’ Stan fit.

     ‘data’ Raw data for the tested group.

_E_x_a_m_p_l_e_s:

     # priors
     mu_prior <- b_prior(family = "normal", pars = c(0, 1000))
     sigma_prior <- b_prior(family = "uniform", pars = c(0, 500))
     nu_prior <- b_prior(family = "normal", pars = c(2000, 1000))
     
     # attach priors to relevant parameters
     priors <- list(
       c("mu", mu_prior),
       c("sigma", sigma_prior),
       c("nu", nu_prior)
     )
     
     # generate data and fit
     data1 <- rnorm(20, mean = 150, sd = 20)
     fit1 <- b_ttest(data = data1, priors = priors, chains = 1)
     
     data2 <- rnorm(20, mean = 200, sd = 20)
     fit2 <- b_ttest(data = data2, priors = priors, chains = 1)
     
     data3 <- rnorm(20, mean = 150, sd = 40)
     fit3 <- b_ttest(data = data3, priors = priors, chains = 1)
     
     data4 <- rnorm(20, mean = 50, sd = 10)
     fit4 <- b_ttest(data = data4, priors = priors, chains = 1)
     
     # fit list
     fit_list <- list(fit2, fit3, fit4)
     
     # a short summary of fitted parameters
     summary(fit1)
     
     # a more detailed summary of fitted parameters
     print(fit1)
     show(fit1)
     
     # plot the fitted distribution against the data
     plot(fit1)
     plot_fit(fit1)
     
     # traceplot of the fitted parameters
     plot_trace(fit1)
     
     # extract parameter values from the fit
     parameters <- get_parameters(fit1)
     
     # compare means between two fits
     compare_means(fit1, fit2 = fit2)
     
     # compare means between two fits, use a rope interval
     compare_means(fit1, fit2 = fit2, rope = 2)
     
     # compare means between a fit and a constant value
     compare_means(fit1, mu = 150)
     
     # compare means between a fit and a distribution,
     # sigma is used for calculating Cohen's d
     compare_means(fit1, mu = 150, sigma = 20)
     
     # compare means between multiple fits
     compare_means(fit1, fits = fit_list)
     
     # visualize difference in means between two fits,
     # specify number of histogram bins
     plot_means_difference(fit1, fit2 = fit2, bins = 20)
     
     # visualize difference in means between a fit and a constant value
     plot_means_difference(fit1, mu = 150)
     
     # visualize difference in means between multiple fits, use a rope interval
     plot_means_difference(fit1, fits = fit_list, rope = 2)
     
     # visualize means of a single fit
     plot_means(fit1)
     
     # visualize means of two fits
     plot_means(fit1, fit2 = fit2)
     
     # visualize means of a fit and a constant value
     plot_means(fit1, mu = 150)
     
     # visualize means of multiple fits
     plot_means(fit1, fits = fit_list)
     
     # draw samples from distributions underlying two fits and compare them
     compare_distributions(fit1, fit2 = fit2)
     
     # draw samples from a distribution underlying the fit
     # and compare them with a constant, use a rope interval
     compare_distributions(fit1, mu = 150, rope = 2)
     
     # draw samples from a distribution underlying the fit and
     # compare them with a user defined distribution
     compare_distributions(fit1, mu = 150, sigma = 20)
     
     # draw samples from distributions underlying multiple fits and compare them
     compare_distributions(fit1, fits = fit_list)
     
     # visualize the distribution underlying a fit
     plot_distributions(fit1)
     
     # visualize distributions underlying two fits
     plot_distributions(fit1, fit2 = fit2)
     
     # visualize the distribution underlying a fit and a constant value
     plot_distributions(fit1, mu = 150)
     
     # visualize the distribution underlying a fit and a user defined distribution
     plot_distributions(fit1, mu = 150, sigma = 20)
     
     # visualize distributions underlying multiple fits
     plot_distributions(fit1, fits = fit_list)
     
     # visualize difference between distributions underlying two fits,
     # use a rope interval
     plot_distributions_difference(fit1, fit2 = fit2, rope = 2)
     
     # visualize difference between a distribution underlying the fit
     # and a constant value
     plot_distributions_difference(fit1, mu = 150)
     
     # visualize difference between a distribution underlying the fits
     # and a user defined distribution
     plot_distributions_difference(fit1, mu = 150, sigma = 20)
     
     # visualize difference between distributions underlying multiple fits
     plot_distributions_difference(fit1, fits = fit_list)
     

> 
> 
> 
> 
> base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
> base::cat("ttest_class-plot_trace", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> ### * <FOOTER>
> ###
> cleanEx()
> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  15.888 1.081 22.095 0.225 0.105 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')
Assertion failure at kmp_runtime.cpp(6918): temp_reg_status_file_name.
OMP: Error #13: Assertion failure at kmp_runtime.cpp(6918).
OMP: Hint Please submit a bug report with this message, compile and run commands used, and machine configuration info including native compiler and operating system versions. Faster response will be obtained by including all program sources. For information on submitting this issue, please see https://bugs.llvm.org/.
Assertion failure at kmp_runtime.cpp(6918): temp_reg_status_file_name.
OMP: Error #13: Assertion failure at kmp_runtime.cpp(6918).
OMP: Hint Please submit a bug report with this message, compile and run commands used, and machine configuration info including native compiler and operating system versions. Faster response will be obtained by including all program sources. For information on submitting this issue, please see https://bugs.llvm.org/.
Killed