Update easybgm 0.5.0

.gitignore

@@ -1,5 +1,8 @@
 .DS_Store
 .Rhistory
 easybgm.Rproj
+easybgm_bgms.Rproj
 .Rproj.user
 auxfunction.R
+Rplots.pdf
+tests/testthat/Rplots.pdf

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: easybgm
 Type: Package
 Title: Extracting and Visualizing Bayesian Graphical Models
-Version: 0.4.0
+Version: 0.5.0
 Authors@R: c(
   person("Karoline", "Huth", , "k.huth@uva.nl", role = c("aut", "cre"), 
         comment = c(ORCID = "0000-0002-0662-1591")), 
@@ -10,11 +10,12 @@ Authors@R: c(
   person("Gali", "Geller", role = c("ctb"))
   )
 Maintainer: Karoline Huth <k.huth@uva.nl>
-Description: Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. 
-    The package supports cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM', 
-    as well as network comparison tests fitted using the packages 'bgms' and 'BBGM'. 
-    The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the 
-    posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior 
+Description: Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology.
+    The package supports cross-sectional network models for ordinal, binary, continuous, and mixed data,
+    fitted using the packages 'bgms' (default), 'BDgraph', and 'BGGM',
+    as well as network comparison tests fitted using the packages 'bgms' and 'BGGM'.
+    The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the
+    posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior
     structure space. Furthermore, the package comes with an extensive suite for visualizing results.
 License: GPL (>= 2)
 URL: https://github.com/KarolineHuth/easybgm
@@ -28,7 +29,7 @@ Depends:
 Imports: 
     BDgraph,
     BGGM,
-    bgms (>= 0.1.4),
+    bgms (>= 0.2.0.0),
     dplyr,
     ggplot2,
     HDInterval,

R/AuxiliaryFunctions.R

@@ -179,6 +179,129 @@ dots_check <- function(...){
   }
 }
 
+# 10. Translate legacy flat prior args into bgms (>= 0.2.0.0) prior objects.
+# Why: bgms now expects prior-class objects (cauchy_prior(), beta_prime_prior(),
+# bernoulli_prior(), etc.). Passing the old flat args still works but triggers
+# lifecycle::deprecate_warn() on every call. Folding them here keeps the
+# easybgm user-facing API unchanged while suppressing those warnings.
+translate_bgm_prior_args <- function(dots) {
+  out <- dots
+
+  # interaction_prior: from pairwise_scale (legacy default 1)
+  if(!"interaction_prior" %in% names(out) && "pairwise_scale" %in% names(out)) {
+    out$interaction_prior <- bgms::cauchy_prior(scale = out$pairwise_scale)
+  }
+  out$pairwise_scale <- NULL
+
+  # threshold_prior: from main_alpha / main_beta (or legacy threshold_alpha / threshold_beta)
+  has_main <- any(c("main_alpha", "main_beta",
+                    "threshold_alpha", "threshold_beta") %in% names(out))
+  if(!"threshold_prior" %in% names(out) && has_main) {
+    a <- out$main_alpha %||% out$threshold_alpha %||% 0.5
+    b <- out$main_beta  %||% out$threshold_beta  %||% 0.5
+    out$threshold_prior <- bgms::beta_prime_prior(alpha = a, beta = b)
+  }
+  out$main_alpha <- out$main_beta <- NULL
+  out$threshold_alpha <- out$threshold_beta <- NULL
+
+  # edge_prior: from character string + flat hyperparameters
+  if("edge_prior" %in% names(out) && is.character(out$edge_prior)) {
+    ep_str <- out$edge_prior
+    ip <- out$inclusion_probability %||% 0.5
+    bba <- out$beta_bernoulli_alpha %||% 1
+    bbb <- out$beta_bernoulli_beta %||% 1
+    bbab <- out$beta_bernoulli_alpha_between %||% 1
+    bbbb <- out$beta_bernoulli_beta_between %||% 1
+    da  <- out$dirichlet_alpha %||% 1
+    lam <- out$lambda %||% 1
+
+    out$edge_prior <- switch(ep_str,
+      "Bernoulli"        = bgms::bernoulli_prior(inclusion_probability = ip),
+      "Beta-Bernoulli"   = bgms::beta_bernoulli_prior(alpha = bba, beta = bbb),
+      "Stochastic-Block" = bgms::sbm_prior(alpha = bba, beta = bbb,
+                                           alpha_between = bbab, beta_between = bbbb,
+                                           dirichlet_alpha = da, lambda = lam),
+      stop("Unknown edge_prior '", ep_str, "'.")
+    )
+  } else if(!"edge_prior" %in% names(out) &&
+            any(c("inclusion_probability", "beta_bernoulli_alpha",
+                  "beta_bernoulli_beta") %in% names(out))) {
+    # User supplied flat hyperparameters without naming the family: assume Bernoulli
+    # if inclusion_probability is given, else Beta-Bernoulli.
+    if("inclusion_probability" %in% names(out)) {
+      out$edge_prior <- bgms::bernoulli_prior(
+        inclusion_probability = out$inclusion_probability
+      )
+    } else {
+      out$edge_prior <- bgms::beta_bernoulli_prior(
+        alpha = out$beta_bernoulli_alpha %||% 1,
+        beta  = out$beta_bernoulli_beta  %||% 1
+      )
+    }
+  }
+  out$inclusion_probability <- NULL
+  out$beta_bernoulli_alpha <- out$beta_bernoulli_beta <- NULL
+  out$beta_bernoulli_alpha_between <- out$beta_bernoulli_beta_between <- NULL
+  out$dirichlet_alpha <- out$lambda <- NULL
+
+  out
+}
+
+# Same for bgmCompare: difference_prior is the indicator prior on group differences.
+translate_bgmcompare_prior_args <- function(dots) {
+  out <- dots
+
+  # interaction_prior (baseline) and threshold_prior: same translation as bgm()
+  if(!"interaction_prior" %in% names(out) && "pairwise_scale" %in% names(out)) {
+    out$interaction_prior <- bgms::cauchy_prior(scale = out$pairwise_scale)
+  }
+  out$pairwise_scale <- NULL
+
+  has_main <- any(c("main_alpha", "main_beta",
+                    "threshold_alpha", "threshold_beta") %in% names(out))
+  if(!"threshold_prior" %in% names(out) && has_main) {
+    a <- out$main_alpha %||% out$threshold_alpha %||% 0.5
+    b <- out$main_beta  %||% out$threshold_beta  %||% 0.5
+    out$threshold_prior <- bgms::beta_prime_prior(alpha = a, beta = b)
+  }
+  out$main_alpha <- out$main_beta <- NULL
+  out$threshold_alpha <- out$threshold_beta <- NULL
+
+  # difference_prior: from a character string + flat hyperparameters
+  if("difference_prior" %in% names(out) && is.character(out$difference_prior)) {
+    dp_str <- out$difference_prior
+    dprob <- out$difference_probability %||% 0.5
+    bba <- out$beta_bernoulli_alpha %||% 1
+    bbb <- out$beta_bernoulli_beta %||% 1
+
+    out$difference_prior <- switch(dp_str,
+      "Bernoulli"      = bgms::bernoulli_prior(inclusion_probability = dprob),
+      "Beta-Bernoulli" = bgms::beta_bernoulli_prior(alpha = bba, beta = bbb),
+      stop("Unknown difference_prior '", dp_str, "'.")
+    )
+  } else if(!"difference_prior" %in% names(out) &&
+            any(c("difference_probability", "beta_bernoulli_alpha",
+                  "beta_bernoulli_beta") %in% names(out))) {
+    if("difference_probability" %in% names(out)) {
+      out$difference_prior <- bgms::bernoulli_prior(
+        inclusion_probability = out$difference_probability
+      )
+    } else {
+      out$difference_prior <- bgms::beta_bernoulli_prior(
+        alpha = out$beta_bernoulli_alpha %||% 1,
+        beta  = out$beta_bernoulli_beta  %||% 1
+      )
+    }
+  }
+  out$difference_probability <- NULL
+  out$beta_bernoulli_alpha <- out$beta_bernoulli_beta <- NULL
+
+  out
+}
+
+# Null-coalescing helper used by the translators above.
+`%||%` <- function(a, b) if(is.null(a)) b else a
+
 
 # Given alpha and beta parameters computes the (log)probability of the beta Bernoulli distribution
 # for the bgms package returns probability for a specific complexity