Merge pull request #257 from snlab-ch/develop

0.13.0

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: migraph
 Title: Tools for Multimodal Network Analysis
-Version: 0.12.6
-Date: 2022-11-09
+Version: 0.13.0
+Date: 2022-11-30
 Description: A set of tools for analysing multimodal networks.
    All functions operate with matrices, edge lists, 
    and 'igraph', 'network', and 'tidygraph' objects,
@@ -19,7 +19,7 @@ License: MIT + file LICENSE
 Language: en-GB
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.2
 Imports: 
     BiocManager,
     dplyr,
@@ -44,6 +44,7 @@ Suggests:
     covr,
     ggdendro,
     knitr,
+    minMSE,
     oaqc,
     readxl,
     Rgraphviz,

NAMESPACE

@@ -91,7 +91,9 @@ S3method(is_weighted,network)
 S3method(is_weighted,tbl_graph)
 S3method(mutate,igraph)
 S3method(plot,diff_model)
+S3method(plot,diffs_model)
 S3method(plot,graph_test)
+S3method(plot,learn_model)
 S3method(plot,matrix)
 S3method(plot,netlm)
 S3method(plot,netlogit)
@@ -100,9 +102,11 @@ S3method(plot,node_measure)
 S3method(plot,node_member)
 S3method(plot,tie_measure)
 S3method(print,diff_model)
+S3method(print,diffs_model)
 S3method(print,graph_measure)
 S3method(print,graph_motif)
 S3method(print,graph_test)
+S3method(print,learn_model)
 S3method(print,network_measure)
 S3method(print,network_motif)
 S3method(print,network_test)
@@ -113,6 +117,8 @@ S3method(print,node_motif)
 S3method(print,tie_mark)
 S3method(print,tie_measure)
 S3method(summary,diff_model)
+S3method(summary,diffs_model)
+S3method(summary,learn_model)
 S3method(summary,node_measure)
 S3method(summary,node_motif)
 S3method(tidy,netlm)
@@ -127,6 +133,16 @@ S3method(to_blocks,igraph)
 S3method(to_blocks,matrix)
 S3method(to_blocks,network)
 S3method(to_blocks,tbl_graph)
+S3method(to_components,data.frame)
+S3method(to_components,igraph)
+S3method(to_components,matrix)
+S3method(to_components,network)
+S3method(to_components,tbl_graph)
+S3method(to_egos,data.frame)
+S3method(to_egos,igraph)
+S3method(to_egos,matrix)
+S3method(to_egos,network)
+S3method(to_egos,tbl_graph)
 S3method(to_giant,data.frame)
 S3method(to_giant,igraph)
 S3method(to_giant,matrix)
@@ -171,6 +187,9 @@ S3method(to_subgraph,igraph)
 S3method(to_subgraph,matrix)
 S3method(to_subgraph,network)
 S3method(to_subgraph,tbl_graph)
+S3method(to_subgraphs,igraph)
+S3method(to_subgraphs,network)
+S3method(to_subgraphs,tbl_graph)
 S3method(to_ties,data.frame)
 S3method(to_ties,igraph)
 S3method(to_ties,matrix)
@@ -221,6 +240,7 @@ export(as_matrix)
 export(as_network)
 export(as_tidygraph)
 export(autographr)
+export(autographs)
 export(bind_edges)
 export(cluster_concor)
 export(cluster_hierarchical)
@@ -292,6 +312,7 @@ export(is.igraph)
 export(is.network)
 export(is.tbl_graph)
 export(is_acyclic)
+export(is_aperiodic)
 export(is_bipartite)
 export(is_complex)
 export(is_connected)
@@ -347,6 +368,7 @@ export(network_node_attributes)
 export(network_nodes)
 export(network_reciprocity)
 export(network_reg)
+export(network_richness)
 export(network_scalefree)
 export(network_smallworld)
 export(network_tie_attributes)
@@ -390,6 +412,7 @@ export(node_reach)
 export(node_reciprocity)
 export(node_redundancy)
 export(node_regular_equivalence)
+export(node_richness)
 export(node_strong_components)
 export(node_structural_equivalence)
 export(node_tie_census)
@@ -398,6 +421,8 @@ export(node_triad_census)
 export(node_walktrap)
 export(node_weak_components)
 export(play_diffusion)
+export(play_diffusions)
+export(play_learning)
 export(read_edgelist)
 export(read_nodelist)
 export(read_pajek)
@@ -421,7 +446,9 @@ export(tie_signs)
 export(tie_weights)
 export(to_anti)
 export(to_blocks)
+export(to_components)
 export(to_edges)
+export(to_egos)
 export(to_giant)
 export(to_main_component)
 export(to_matching)
@@ -433,6 +460,7 @@ export(to_onemode)
 export(to_redirected)
 export(to_simplex)
 export(to_subgraph)
+export(to_subgraphs)
 export(to_ties)
 export(to_twomode)
 export(to_undirected)
@@ -470,6 +498,7 @@ importFrom(ggplot2,arrow)
 importFrom(ggplot2,coord_flip)
 importFrom(ggplot2,element_blank)
 importFrom(ggplot2,element_text)
+importFrom(ggplot2,geom_histogram)
 importFrom(ggplot2,geom_hline)
 importFrom(ggplot2,geom_tile)
 importFrom(ggplot2,geom_vline)
@@ -534,6 +563,7 @@ importFrom(igraph,graph.neighborhood)
 importFrom(igraph,graph_from_adjacency_matrix)
 importFrom(igraph,graph_from_data_frame)
 importFrom(igraph,graph_from_incidence_matrix)
+importFrom(igraph,induced_subgraph)
 importFrom(igraph,is.bipartite)
 importFrom(igraph,is.connected)
 importFrom(igraph,is.directed)
@@ -546,6 +576,7 @@ importFrom(igraph,is_bipartite)
 importFrom(igraph,is_dag)
 importFrom(igraph,list.edge.attributes)
 importFrom(igraph,list.vertex.attributes)
+importFrom(igraph,make_ego_graph)
 importFrom(igraph,make_lattice)
 importFrom(igraph,make_line_graph)
 importFrom(igraph,make_star)
@@ -574,6 +605,7 @@ importFrom(network,as.network)
 importFrom(network,is.network)
 importFrom(network,read.paj)
 importFrom(patchwork,plot_layout)
+importFrom(patchwork,wrap_plots)
 importFrom(purrr,flatten)
 importFrom(rlang,":=")
 importFrom(rlang,.data)

NEWS.md

@@ -1,3 +1,72 @@
+# migraph 0.13.0
+
+## Package
+
+- Added hints and questions to community tutorial
+- Added diffusion tutorial
+
+## Marks
+
+- Added `is_aperiodic()` for testing whether a network is aperiodic 
+(the greatest common divisor for all cycles in the network is 1)
+  - Note that for computational efficiency, this will check only up to a specified
+  path length (by default 4)
+- `node_is_max()` and `node_is_min()` now take a "rank" argument for selecting more
+than the first ranked maxima or minima
+
+## Manipulations
+
+- Added several new functions for splitting networks into a list of networks
+  - Added `to_components()` to return the components of a network as a list of networks
+  - Added `to_egos()` to return the ego networks of a network as a list of networks
+  - Added `to_subgraphs()` to return attribute-based subgraphs as a list of networks
+
+## Measures
+
+- Added `network_richness()` and `node_richness()` for calculating the richness 
+(a common diversity measure) of an attribute in a network
+
+## Models
+
+- Extended `play_diffusion()` to include more compartment and transition options
+  - The print method now tabulates the compartment sums per step
+  - The summary method now presents the diffusion event list
+  - The plot method now:
+    - plots lines for S and I compartments, as blue and red lines respectively, 
+    if available or informs the user if no diffusion could be simulated
+    - plots lines for E and R compartments, as orange and green lines respectively, if relevant
+    - plots a bar graph behind showing the number of new infections per step
+  - Added "transmissibility" parameter to allow for more probabilistic contagion,
+  by default 1 (all contacts over the threshold result in contagion)
+  - Added "latency" parameter to allow for an Exposed compartment to be included,
+  by default 0 (no incubation period)
+  - Added "recovery" rate parameter to allow for a Recovered compartment to be included,
+  by default 0 (no recovery)
+  - Added "waning" parameter to allow for returns to the Susceptible compartment,
+  by default 0 (any recovered have lifelong immunity)
+  - Added "immune" parameter to allow for issuing of targetted vaccinations or related
+  - "thresholds" can now be proportions, in which case they are interpreted as complex
+  - "seeds" is now 1 by default
+- Added `play_diffusions()` for running a diffusion model multiple times
+  - Note for accelerating documentation and tutorials, this is 5 by default,
+  but for publication quality results this should be increased
+  - A print method tabulates the compartment sums per step per simulation
+  - A summary method tabulates the steps until complete infection (or recovery)
+  for each simulation
+  - A plot method visualises the loess for each compartment across all simulations
+  - Note that this function uses `{furrr}` and so a multicore or multisession strategy
+  can be used for parallelisation (but this only makes sense for many simulations)
+- Added `play_learning()` for running a DeGroot learning model
+  - A print method tabulates nodes beliefs at each step
+  - A summary method informs how many steps it took until convergence or
+  whether there was no convergence after _t_ steps
+  - A plot method visualises the belief trajectories for each node
+- `network_reg()` now declares the reference category for nominal variables
+
+## Mapping
+
+- Added `autographs()` for autoplotting lists of networks
+
 # migraph 0.12.6
 
 ## Package