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__default_factor and
__default_continuous priors in JAGS_formula()
- when specified in the prior_list, these are used as
default priors for factor and continuous predictors that are not
explicitly specifiedformula_scale parameter in JAGS_formula() and
JAGS_fit() - improves MCMC sampling efficiency and
numerical stabilitytransform_scale_samples() function to transform
posterior samples back to original scale after standardizationtransform_prior_samples() function to generate and
transform prior samples using the same matrix transformation as
posterior samples - enables correct visualization of priors on the
original (unscaled) predictor scale, including proper handling of the
intercept which depends on multiple coefficient priorstransform_scaled argument to
plot_posterior() for visualizing prior and posterior
distributions on the original (unscaled) scale when using formula-based
models with auto-scalingexp_lin transformation type for log-intercept
unscaling in density/plotting functions:
exp(a + b * log(x))log(intercept) formula attribute for specifying
models of the form log(intercept) + sum(beta_i * x_i) -
useful for parameters that must be positive (e.g., standard deviation)
while keeping the intercept on the original scale. Set via
attr(formula, "log(intercept)") <- TRUE. Supported in
JAGS_formula(), JAGS_evaluate_formula(), and
marginal likelihood computationrunjags_estimates_table():
remove_parameters = TRUE to remove all non-formula
parametersremove_formulas to remove all parameters from specific
formulaskeep_parameters to keep only specified parameterskeep_formulas to keep only parameters from specified
formulasbias is specified in
remove_parameters or keep_parameters, the
corresponding bias-related parameters (PET,
PEESE, omega, alpha,
pi_null, and phack_kind) are automatically
included based on the bias prior typeprobs argument to
runjags_estimates_table() and
runjags_estimates_empty_table() for custom quantiles
(default: c(0.025, 0.5, 0.975))effect_direction argument to
plot_posterior(), plot_prior_list(),
lines_prior_list(), and geom_prior_list() for
PET-PEESE regression plots - use "positive" (default) for
mu + PET*se + PEESE*se^2 or "negative" for
mu - PET*se - PEESE*se^2prior_weightfunction() around a unified
side, steps, and weights
specification, with wf_cumulative(),
wf_fixed(), and wf_independent() constructors
for cumulative Dirichlet, fixed, independent, and log-independent
weightfunction priorsprior_phacking(), prior_bias(), calibration
helpers, and selection_backend_spec() for compiling active
step/p-hacking backend parametersrunjags_estimates_table() and
stan_estimates_table() from
lCI/Median/uCI to numeric values
(e.g., 0.025/0.5/0.975) for
consistency with ensemble summary tablesomega
representationNA when the prior
assigns probability 0 or 1 to inclusion, while keeping finite-sample
bounds for posterior inclusion probabilities of 0 or 10 (instead of
only -1).is.wholenumber with NAs and
na.rm = TRUEJAGS_diagnostics functions now correctly handle factor
parameters nested within mixture priorsplot_posterior() function with spike and slab
priorsprior_mixture() and
prior_spike_and_slab()JAGS_formula() function now replaces removed missing
intercept with 0 (so the model matrix remains unchanged)silent = FALSE argument in the
JAGS_fit() function now fits the model non-silently
againexpression()
instead of a parameter, such objects can be use to create prior
distributions that depend on other parameters in JAGSJAGS_fit() function
to accept expressions that are appended as literal text to the generated
JAGS formulaJAGS_fit() function
to handle uncorrelated random effects via (x||y)
(lme4-like) notationJAGS_estimates_table not printing formula prefix when
only spike and slab priors are suppliedmax_extend option to autofit_control
argument in JAGS_fit() to limit the number of iterations
for the model extensionJAGS_diagnostics_density() plots for mixture
distributionsplot_posterior() for simple
as_mixed_posteriors objectsJAGS_evaluate_formula() for mixture and spike and slab
priors.fit_to_posterior()prior_mixture() function for creating a mixture
of prior distributionsas_mixed_posteriors() and
as_marginal_inference() functions for a single JAGS models
(with spike and slab or mixture priors) to enabling tables and figures
based on the corresponding outputinterpret2() function for another way of
creating textual summaries without the need of inference and samples
objectsrunjags_estimates_table() functionprior_informed() functionbridge_object() (fixes:
https://github.com/FBartos/BayesTools/issues/28)Na/NaN tests for check_ functions
(fixes: https://github.com/FBartos/BayesTools/issues/26)JAGS_extend()
functionautofit_control argument in
JAGS_fit(): "restarts" allows to restart model
initialization up to restarts times in case of failuremodel_summary_table() in case of
prior_none()contrast = "meandif" to the
prior_factor function which generates identical prior
distributions for difference between the grand mean and each factor
levelcontrast = "independent" to the
prior_factor function which generates independent identical
prior distributions for each factor levelremove_column function for removing columns from
BayesTools_table objects without breaking the attributes
etc…remove_parameters argument to
model_summary_table()point prior distribution as option to
prior_factor with "meandif" and
"orthonormal" contrastsmarginal_posterior() function which creates
marginal prior and posterior distributions (according to a model formula
specification)Savage_Dickey_BF() function to compute density
ratio Bayes factors based on marginal_posterior
objectsmarginal_inference() function to combine
information from marginal_posterior() and
Savage_Dickey_BF()marginal_estimates_table() function to summarize
marginal_inference() objectsplot_marginal() function to visualize
marginal_inference() objectscontrast = "meandif" is now the default setting for
prior_factor functiontransform_orthonormal argument in favor of
more general transform_factors argumentdummy contrast/factor attributes to
treatment for consistency
(https://github.com/FBartos/BayesTools/issues/23)check_bool(),
check_char(), check_real(),
check_int(), and check_list() do not throw
error if allow_NULL = TRUEstudent-t allowed as a prior distribution
nameJAGS_evaluate_formularunjags_estimates_table() function can now handle
factor transformationsplot_posterior function can now handle factor
transformationsrunjags_estimates_table() function via the
remove_parameters argumentrunjags_estimates_table() function can now remove
factor spike prior distributionsstan_estimates_summary() functionJAGS_marglik_parameters_formula functionrunjags_estimates_table functionensemble_summary_table and
ensemble_diagnostics_table function can create table
without model componentsJAGS_evaluate_formula for evaluating formulas based on
data and posterior samples (for creating predictions etc)JAGS_parameter_names for transforming formula names
into the JAGS syntaxplot_models implementation for factor predictorsformat_parameter_names for cleaning parameter names
from JAGSmean, sd, and var functions
now return the corresponding values for differences from the mean for
the orthonormal prior distributionsrunjags_summary_table function
(previous version crashed under other than default fit_JAGS
settings)runjags_summary_table functionplot_models functionadd_column function for extending
BayesTools_table objects without breaking the attributes
etc…BayesTools_table functions with with
formula_prefix argumentmultiply_by attribute passed with the
prior)plot_posterior (posterior is now plotted over the
prior)inclusion_BF to deal with
over/underflow (Issue #9)ensemble_inference_table() (Issue #11)ensemble_summary_table
(Issue #7)prior_informed function for creating informed prior
distributions based on the past psychological and medical researchprior.plot can’t plot “spike” with
plot_type == "ggplot" (Issue #6)MCMC error/SD print names in BayesTools tables (Issue
#8)JAGS_bridgesampling_posterior unable to add a parameter
via add_parametersinterpret function for creating textual summaries based
on inference and samples objectsplot_posterior fails with only mu & PET samples
(Issue #5)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.