updated version number + author list

Author: TGuillerme

DESCRIPTION

@@ -3,12 +3,13 @@ Title: Measuring Disparity
 Authors@R: c(person("Thomas", "Guillerme", role = c("aut", "cre", "cph"),
 										   email = "guillert@tcd.ie",
 										   comment = c(ORCID = "0000-0003-4325-1275")),
+  			 person("Jack", "Hatfield",    role = c("aut", "cph")),
   			 person("Mark", "Puttick",     role = c("aut", "cph")),
-  			 person("Jack", "Hatfield",    role = c("aut", "cph"))
+  			 person("Caleb", "Scutt",      role = c("aut", "cph"))
   			 )
 Maintainer: Thomas Guillerme <guillert@tcd.ie>
-Version: 1.9.7
-Date: 2025-08-22
+Version: 1.9.8
+Date: 2025-12-08
 Description: A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis.
 Depends:
     R (>= 3.6.0),

NEWS.md

@@ -1,10 +1,8 @@
-dispRity v1.9.8 (2025-12-037)
+dispRity v1.9.8 (2025-12-08)
 =========================
 
 ### NEW FEATURES
- * **Major restructure** of `multi.ace`: the `threshold` and `sample` arguments have been replaced by a single `ml.collapse` argument that takes a named list structure. This now combines the previously separate arguments `threshold` and `sample` under one banner.
- * Use `ml.collapse = list(type = "majority")` (replaces `threshold = "max"`), `ml.collapse = list(type = "relative")` (replaces `threshold = TRUE`), `ml.collapse = list(type = "threshold", threshold = 0.95)` (replaces `threshold = 0.95`), or `ml.collapse = list(type = "sample", sample = 100)` (replaces `sample = 100`).
- * New tie-breaking option in `multi.ace`: `ml.collapse = list(type = "majority", tie.breaker = TRUE)` for randomly selecting one discrete state when highest scaled likelihoods are tied (alternative to default `tie.breaker = FALSE` which returns all tied states with uncertainty token).
+ * **Major restructure** of `multi.ace`: the `threshold` and `sample` arguments have been replaced by a single `ml.collapse` argument that takes a named list structure. This now combines the previously separate arguments `threshold` and `sample` under one banner. Use `ml.collapse = list(type = "majority")` (replaces `threshold = "max"`), `ml.collapse = list(type = "relative")` (replaces `threshold = TRUE`), `ml.collapse = list(type = "threshold", threshold = 0.95)` (replaces `threshold = 0.95`), or `ml.collapse = list(type = "sample", sample = 100)` (replaces `sample = 100`). This also includes a new tie-breaking option in `multi.ace`: `ml.collapse = list(type = "majority", tie.breaker = TRUE)` for randomly selecting one discrete state when highest scaled likelihoods are tied (alternative to default `tie.breaker = FALSE` which returns all tied states with uncertainty token).
 
 ### MINOR IMPROVEMENTS
  * **Changed default argument** in `multi.ace`: `ml.collapse` default is now `list(type = "majority")` (previously `threshold = TRUE` which mapped to `"relative"`).