#minor bump and updated description to match package trim

DESCRIPTION

@@ -1,24 +1,24 @@
 Package: migraph
-Title: Many Network Measures, Motifs, Members, and Models
+Title: Univariate and multivariate tests for multimodal and other networks 
 Version: 1.4.0
-Date: 2024-07-08
-Description: A set of tools for analysing multimodal networks.
- It includes functions for measuring 
- centrality, centralization, cohesion, closure, constraint and diversity,
- as well as for network block-modelling, regression, and diffusion models.
- The package is released as a complement to 
+Date: 2024-07-18
+Description: A set of tools for testing networks.
+ It includes functions for univariate and multivariate 
+ conditional uniform graph and quadratic assignment procedure testing,
+ and network regression.
+ The package is a complement to 
  'Multimodal Political Networks' (2021, ISBN:9781108985000),
  and includes various datasets used in the book.
  Built on the 'manynet' package, all functions operate with matrices, edge lists, 
  and 'igraph', 'network', and 'tidygraph' objects,
- and on one-mode, two-mode (bipartite), and sometimes three-mode networks.
+ and on one-mode and two-mode (bipartite) networks.
 URL: https://stocnet.github.io/migraph/
 BugReports: https://github.com/stocnet/migraph/issues
 License: MIT + file LICENSE
 Language: en-GB
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Depends:
  R (>= 3.6.0),
  manynet
@@ -68,4 +68,3 @@ Roxygen: list(markdown = TRUE, roclets = c("namespace", "rd"))
 Config/testthat/parallel: true
 Config/testthat/edition: 3
 Config/testthat/start-first: helper-functions 
-

NEWS.md

@@ -1,45 +1,36 @@
 # migraph 1.4.0
 
-2024-05-23
+2024-07-18
 
 ## Package
 
 - Updated migraph logo with stocnet address, colorsafe colorway, and larger nodes and ties
 - Copied thisRequires() helper into migraph from manynet
+- testthat tests now parallelised
+- Fixed precision issues in testthat tests
+- Declared global variables `.data` and `.graph_context`
 
-## Measures
-
-- All measure functions can now be used in e.g. `mutate()` without specifying `.data`
+## Measures, Motifs, and Memberships
 
-## Members
+- All measures, motifs, and memberships have migrated to `{manynet}`
+ - see `{manynet}` > v1.0.0 for more details
 
-- All membership functions can now be used in e.g. `mutate()` without specifying `.data`
-- The `node_member` class is now categorical
- - `make_node_member()` now converts numeric results to LETTER character results
- - `print.node_member()` now works with categorical membership vectors
- - `print.node_member()` now declares how many groups before reporting the vectors
-- Fixed `node_constraint()` to work with weighted two-mode networks, thanks to Toshitaka Izumi for spotting this
-- Added `network_independence()` for calculating the number of nodes in the largest independent set
+## Models
 
-## Motifs
+- Added `test_distribution()` to test whether two vectors/distributions are from the same distribution
+- `test_gof()` renamed to `test_fit()` to improve readability
+ - Split tests documentation into two
+ - `test_fit()` no longer measures fit against steps where there is no covariance
+- Fixed bug in `test_random()` where parameters were passed to `manynet::generate_random()` instead of the original object, which is processed more intuitively within `manynet::generate_random()` (thanks @RWKrause)
+- Corrected that `test_random()` returns results on edge-conditioned uniform graphs, not size
+- Reexported `ggplot2::scale_y_discrete()`
+- Specification advice in `network_reg()` now ignores absent ego terms for undirected networks
 
-- `node_tie_census()` now works on longitudinal network data
-- Added `to_correlation()` for calculating the Pearson correlation
- - This takes a method argument for "all", "diag", "recip", or "complex"
- - These are similar to functions implemented by Ron Breiger and shared by him in correspondence
- - 
-- Fixed bug where `print.node_motif()` wasn't printing the requested number of lines
+## Tutorials
 
-## Models
+- Added descriptions to tutorials
+- Renamed regression tutorial the diversity tutorial
 
-- Several improvements to `cluster_concor()`
- - `cluster_concor()` now uses `to_correlation()` for initial correlation
- - It still uses `stats::cor()` for subsequent iterations
- - Fixed how `cluster_concor()` handles unlabelled networks
- - Fixed how `cluster_concor()` handles two-mode networks
- - Fixed bug where `cluster_concor()` cutoff resulted in unsplit groups
-- `cluster_hierarchical()` now also uses `to_correlation()`
-
 # migraph 1.3.4
 
 2024-03-07