refactor: move optional arguments of hrg-embedding functions behind the ellipsis

R/cycles.R

@@ -31,6 +31,7 @@
 #' a specific cycle.
 #'
 #' @param graph The input graph.
+#' @inheritParams rlang::args_dots_empty
 #' @param mode Character constant specifying how to handle directed graphs.
 #'   `out` follows edge directions, `in` follows edges in the reverse direction,
 #'   and `all` ignores edge directions. Ignored in undirected graphs.

R/embedding.R

@@ -48,6 +48,7 @@
 #'   spectral embedding. Should be smaller than the number of vertices. The
 #'   largest `no`-dimensional non-zero singular values are used for the
 #'   spectral embedding.
+#' @inheritParams rlang::args_dots_empty
 #' @param weights Optional positive weight vector for calculating a weighted
 #'   embedding. If the graph has a `weight` edge attribute, then this is
 #'   used by default. In a weighted embedding, the edge weights are used instead
@@ -104,12 +105,47 @@
 embed_adjacency_matrix <- function(
   graph,
   no,
+  ...,
   weights = NULL,
   which = c("lm", "la", "sa"),
   scaled = TRUE,
   cvec = strength(graph, weights = weights) / (vcount(graph) - 1),
   options = arpack_defaults()
 ) {
+  # BEGIN GENERATED ARG_HANDLE: embed_adjacency_matrix, do not edit, see tools/generate-migrations.R
+  if (...length() > 0L) {
+    .arg_handle <- migrate_recover_args(
+      list(...),
+      current = list(
+        weights = weights,
+        which = which,
+        scaled = scaled,
+        cvec = cvec,
+        options = options
+      ),
+      recover_new = c("weights", "which", "scaled", "cvec", "options"),
+      recover_old = c("weights", "which", "scaled", "cvec", "options"),
+      match_names = c("weights", "which", "scaled", "cvec", "options"),
+      match_to = c("weights", "which", "scaled", "cvec", "options"),
+      defaults = list(
+        weights = NULL,
+        which = c("lm", "la", "sa"),
+        scaled = TRUE,
+        cvec = strength(graph, weights = weights) / (vcount(graph) - 1),
+        options = arpack_defaults()
+      ),
+      head_args = c("graph", "no"),
+      fn_name = "embed_adjacency_matrix"
+    )
+    list2env(.arg_handle$values, environment())
+    lifecycle::deprecate_soft(
+      "3.0.0",
+      what = I(.arg_handle$what),
+      details = .arg_handle$details
+    )
+  }
+  # END GENERATED ARG_HANDLE
+
   adjacency_spectral_embedding_impl(
     graph = graph,
     no = no,
@@ -203,6 +239,7 @@ dim_select <- function(sv) {
 #'   spectral embedding. Should be smaller than the number of vertices. The
 #'   largest `no`-dimensional non-zero singular values are used for the
 #'   spectral embedding.
+#' @inheritParams rlang::args_dots_empty
 #' @param weights Optional positive weight vector for calculating a weighted
 #'   embedding. If the graph has a `weight` edge attribute, then this is
 #'   used by default. For weighted embedding, edge weights are used instead
@@ -278,12 +315,47 @@ dim_select <- function(sv) {
 embed_laplacian_matrix <- function(
   graph,
   no,
+  ...,
   weights = NULL,
   which = c("lm", "la", "sa"),
   type = c("default", "D-A", "DAD", "I-DAD", "OAP"),
   scaled = TRUE,
   options = arpack_defaults()
 ) {
+  # BEGIN GENERATED ARG_HANDLE: embed_laplacian_matrix, do not edit, see tools/generate-migrations.R
+  if (...length() > 0L) {
+    .arg_handle <- migrate_recover_args(
+      list(...),
+      current = list(
+        weights = weights,
+        which = which,
+        type = type,
+        scaled = scaled,
+        options = options
+      ),
+      recover_new = c("weights", "which", "type", "scaled", "options"),
+      recover_old = c("weights", "which", "type", "scaled", "options"),
+      match_names = c("weights", "which", "type", "scaled", "options"),
+      match_to = c("weights", "which", "type", "scaled", "options"),
+      defaults = list(
+        weights = NULL,
+        which = c("lm", "la", "sa"),
+        type = c("default", "D-A", "DAD", "I-DAD", "OAP"),
+        scaled = TRUE,
+        options = arpack_defaults()
+      ),
+      head_args = c("graph", "no"),
+      fn_name = "embed_laplacian_matrix"
+    )
+    list2env(.arg_handle$values, environment())
+    lifecycle::deprecate_soft(
+      "3.0.0",
+      what = I(.arg_handle$what),
+      details = .arg_handle$details
+    )
+  }
+  # END GENERATED ARG_HANDLE
+
   laplacian_spectral_embedding_impl(
     graph = graph,
     no = no,
@@ -307,6 +379,7 @@ embed_laplacian_matrix <- function(
 #'
 #' @param dim Integer scalar, the dimension of the random vectors.
 #' @param n Integer scalar, the sample size.
+#' @inheritParams rlang::args_dots_empty
 #' @param radius Numeric scalar, the radius of the sphere to sample.
 #' @param positive Logical, whether to sample from the positive orthant
 #'   of the sphere.
@@ -324,7 +397,35 @@ embed_laplacian_matrix <- function(
 #'   sum(x^2)
 #' })
 #' vec.norm
-sample_sphere_surface <- function(dim, n = 1, radius = 1, positive = TRUE) {
+sample_sphere_surface <- function(
+  dim,
+  n = 1,
+  ...,
+  radius = 1,
+  positive = TRUE
+) {
+  # BEGIN GENERATED ARG_HANDLE: sample_sphere_surface, do not edit, see tools/generate-migrations.R
+  if (...length() > 0L) {
+    .arg_handle <- migrate_recover_args(
+      list(...),
+      current = list(radius = radius, positive = positive),
+      recover_new = c("radius", "positive"),
+      recover_old = c("radius", "positive"),
+      match_names = c("radius", "positive"),
+      match_to = c("radius", "positive"),
+      defaults = list(radius = 1, positive = TRUE),
+      head_args = c("dim", "n"),
+      fn_name = "sample_sphere_surface"
+    )
+    list2env(.arg_handle$values, environment())
+    lifecycle::deprecate_soft(
+      "3.0.0",
+      what = I(.arg_handle$what),
+      details = .arg_handle$details
+    )
+  }
+  # END GENERATED ARG_HANDLE
+
   # Use the _impl function
   sample_sphere_surface_impl(
     dim = dim,
@@ -345,6 +446,7 @@ sample_sphere_surface <- function(dim, n = 1, radius = 1, positive = TRUE) {
 #'
 #' @param dim Integer scalar, the dimension of the random vectors.
 #' @param n Integer scalar, the sample size.
+#' @inheritParams rlang::args_dots_empty
 #' @param radius Numeric scalar, the radius of the sphere to sample.
 #' @param positive Logical, whether to sample from the positive orthant
 #'   of the sphere.
@@ -362,7 +464,35 @@ sample_sphere_surface <- function(dim, n = 1, radius = 1, positive = TRUE) {
 #'   sum(x^2)
 #' })
 #' vec.norm
-sample_sphere_volume <- function(dim, n = 1, radius = 1, positive = TRUE) {
+sample_sphere_volume <- function(
+  dim,
+  n = 1,
+  ...,
+  radius = 1,
+  positive = TRUE
+) {
+  # BEGIN GENERATED ARG_HANDLE: sample_sphere_volume, do not edit, see tools/generate-migrations.R
+  if (...length() > 0L) {
+    .arg_handle <- migrate_recover_args(
+      list(...),
+      current = list(radius = radius, positive = positive),
+      recover_new = c("radius", "positive"),
+      recover_old = c("radius", "positive"),
+      match_names = c("radius", "positive"),
+      match_to = c("radius", "positive"),
+      defaults = list(radius = 1, positive = TRUE),
+      head_args = c("dim", "n"),
+      fn_name = "sample_sphere_volume"
+    )
+    list2env(.arg_handle$values, environment())
+    lifecycle::deprecate_soft(
+      "3.0.0",
+      what = I(.arg_handle$what),
+      details = .arg_handle$details
+    )
+  }
+  # END GENERATED ARG_HANDLE
+
   # Use the _impl function
   sample_sphere_volume_impl(
     dim = dim,

R/hrg.R

@@ -177,6 +177,7 @@ NULL
 #' @param hrg A hierarchical random graph model, in the form of an
 #'   `igraphHRG` object. `fit_hrg()` allows this to be `NULL`, in
 #'   which case a random starting point is used for the fitting.
+#' @inheritParams rlang::args_dots_empty
 #' @param start Logical, whether to start the fitting/sampling from the
 #'   supplied `igraphHRG` object, or from a random starting point.
 #' @param steps The number of MCMC steps to make. If this is zero, then the
@@ -232,7 +233,35 @@ NULL
 #' predict_edges(g2)
 #' @export
 #' @family hierarchical random graph functions
-fit_hrg <- function(graph, hrg = NULL, start = FALSE, steps = 0) {
+fit_hrg <- function(
+  graph,
+  hrg = NULL,
+  ...,
+  start = FALSE,
+  steps = 0
+) {
+  # BEGIN GENERATED ARG_HANDLE: fit_hrg, do not edit, see tools/generate-migrations.R
+  if (...length() > 0L) {
+    .arg_handle <- migrate_recover_args(
+      list(...),
+      current = list(start = start, steps = steps),
+      recover_new = c("start", "steps"),
+      recover_old = c("start", "steps"),
+      match_names = c("start", "steps"),
+      match_to = c("start", "steps"),
+      defaults = list(start = FALSE, steps = 0),
+      head_args = c("graph", "hrg"),
+      fn_name = "fit_hrg"
+    )
+    list2env(.arg_handle$values, environment())
+    lifecycle::deprecate_soft(
+      "3.0.0",
+      what = I(.arg_handle$what),
+      details = .arg_handle$details
+    )
+  }
+  # END GENERATED ARG_HANDLE
+
   # Argument checks
   ensure_igraph(graph)
   if (is.null(hrg)) {
@@ -281,6 +310,7 @@ fit_hrg <- function(graph, hrg = NULL, start = FALSE, steps = 0) {
 #'   `igraphHRG` object. `consensus_tree()` allows this to be
 #'   `NULL` as well, then a HRG is fitted to the graph first, from a
 #'   random starting point.
+#' @inheritParams rlang::args_dots_empty
 #' @param start Logical, whether to start the fitting/sampling from the
 #'   supplied `igraphHRG` object, or from a random starting point.
 #' @param num.samples Number of samples to use for consensus generation or
@@ -307,9 +337,32 @@ fit_hrg <- function(graph, hrg = NULL, start = FALSE, steps = 0) {
 consensus_tree <- function(
   graph,
   hrg = NULL,
+  ...,
   start = FALSE,
   num.samples = 10000
 ) {
+  # BEGIN GENERATED ARG_HANDLE: consensus_tree, do not edit, see tools/generate-migrations.R
+  if (...length() > 0L) {
+    .arg_handle <- migrate_recover_args(
+      list(...),
+      current = list(start = start, num.samples = num.samples),
+      recover_new = c("start", "num.samples"),
+      recover_old = c("start", "num.samples"),
+      match_names = c("start", "num.samples"),
+      match_to = c("start", "num.samples"),
+      defaults = list(start = FALSE, num.samples = 10000),
+      head_args = c("graph", "hrg"),
+      fn_name = "consensus_tree"
+    )
+    list2env(.arg_handle$values, environment())
+    lifecycle::deprecate_soft(
+      "3.0.0",
+      what = I(.arg_handle$what),
+      details = .arg_handle$details
+    )
+  }
+  # END GENERATED ARG_HANDLE
+
   hrg_consensus_impl(
     graph = graph,
     hrg = hrg,
@@ -403,6 +456,7 @@ sample_hrg <- function(hrg) {
 #'   `igraphHRG` object. `predict_edges()` allow this to be
 #'   `NULL` as well, then a HRG is fitted to the graph first, from a
 #'   random starting point.
+#' @inheritParams rlang::args_dots_empty
 #' @param start Logical, whether to start the fitting/sampling from the
 #'   supplied `igraphHRG` object, or from a random starting point.
 #' @param num.samples Number of samples to use for consensus generation or
@@ -448,10 +502,37 @@ sample_hrg <- function(hrg) {
 predict_edges <- function(
   graph,
   hrg = NULL,
+  ...,
   start = FALSE,
   num.samples = 10000,
   num.bins = 25
 ) {
+  # BEGIN GENERATED ARG_HANDLE: predict_edges, do not edit, see tools/generate-migrations.R
+  if (...length() > 0L) {
+    .arg_handle <- migrate_recover_args(
+      list(...),
+      current = list(
+        start = start,
+        num.samples = num.samples,
+        num.bins = num.bins
+      ),
+      recover_new = c("start", "num.samples", "num.bins"),
+      recover_old = c("start", "num.samples", "num.bins"),
+      match_names = c("start", "num.samples", "num.bins"),
+      match_to = c("start", "num.samples", "num.bins"),
+      defaults = list(start = FALSE, num.samples = 10000, num.bins = 25),
+      head_args = c("graph", "hrg"),
+      fn_name = "predict_edges"
+    )
+    list2env(.arg_handle$values, environment())
+    lifecycle::deprecate_soft(
+      "3.0.0",
+      what = I(.arg_handle$what),
+      details = .arg_handle$details
+    )
+  }
+  # END GENERATED ARG_HANDLE
+
   res <- hrg_predict_impl(
     graph = graph,
     hrg = hrg,