515: release 0.3.15 (#516)

* version bump, clean news

* use 4.1 as min R version

Author: danielinteractive

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: mmrm
 Title: Mixed Models for Repeated Measures
-Version: 0.3.14.9002
+Version: 0.3.15
 Authors@R: c(
     person("Daniel", "Sabanes Bove", , "daniel.sabanes_bove@rconis.com", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-0176-9239")),
@@ -46,7 +46,7 @@ License: Apache License 2.0
 URL: https://openpharma.github.io/mmrm/
 BugReports: https://github.com/openpharma/mmrm/issues
 Depends:
-    R (>= 4.0)
+    R (>= 4.1)
 Imports:
     checkmate (>= 2.0),
     generics,

NEWS.md

@@ -1,14 +1,14 @@
-# mmrm 0.3.14.9002
+# mmrm 0.3.15
+
+### New Features
+
+- `mmrm` now returns score per subject in empirical covariance. It can be accessed by `component(obj, name = "score_per_subject")`. 
 
 ### Bug Fixes
 
 - Previously, when fitting a model with empirical covariance matrix estimation to a data set with a large number of subjects and/or a large number of coefficients, the model fitting could take very long and exhaust the memory in the worst case. This was due to an inefficient implementation of a matrix needed only in case of Satterthwaite degrees of freedom adjustment. This is fixed now, by returning the matrix `empirical_g_mat` in the `mmrm` object, instead of the previous `empirical_df_mat` matrix. The model fit is now much faster and does not exhaust the memory anymore. If old model fit objects are used, the `empirical_df_mat` will still be used correctly, however a deprecation warning will be issued. Please consider re-fitting the model to get the new `empirical_g_mat` matrix.
 - Previously, when compiling `mmrm` from source using a `TMB` version below 1.9.15, and installing a newer `TMB` of version 1.9.15 or above, would render the `mmrm` package unusable. This is fixed now, by checking in the dynamic library of `mmrm` whether the version of `TMB` has been sufficient.
 
-### New Features
-
-- `mmrm` now returns score per subject in empirical covariance. It can be accessed by `component(obj, name = "score_per_subject")`. 
-
 # mmrm 0.3.14
 
 ### Bug Fixes