fix CRAN additional issues error with ATLAS

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: posologyr
 Title: Individual Dose Optimization using Population Pharmacokinetics
-Version: 1.2.7
+Version: 1.2.8
 Authors@R: c(
     person("Cyril","Leven",role = c("aut", "cre","cph"),
            email = "cyril.leven@chu-brest.fr",
@@ -12,13 +12,12 @@ Authors@R: c(
     person("Audrey","Lavenu",role = c("ctb")),
     person("Marc","Lavielle",role = c("ctb"))
     )
-Description: Optimize drug regimens through model-informed precision dosing, 
-    using individual pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic 
-    (PK-PD) profiles. By integrating therapeutic drug monitoring (TDM) data with
-    population models, 'posologyr' provides accurate posterior estimates and 
-    enables the calculation of personalized dosing regimens. The empirical Bayes
-    estimates are computed following the method described by Kang et al. (2012) 
-    <doi:10.4196/kjpp.2012.16.2.97>.
+Description: Personalize drug regimens using individual pharmacokinetic (PK) and 
+    pharmacokinetic-pharmacodynamic (PK-PD) profiles. By combining therapeutic 
+    drug monitoring (TDM) data with a population model, 'posologyr' offers 
+    accurate posterior estimates and helps compute optimal individualized dosing
+    regimens. The empirical Bayes estimates are computed following the method 
+    described by Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97>.
 License: AGPL-3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)

NEWS.md

@@ -1,3 +1,6 @@
+# posologyr v1.2.8
+* Relax the requirements of a dose optimization test using TDM data to account for computational differences observed with the alternative BLAS ATLAS on CRAN.
+
 # posologyr v1.2.7
 
 * All vignettes are moved to articles
@@ -52,7 +55,7 @@ with interpolation of covariates, to make plotting easier
 
 ## Additional features
 * `poso_estim_map()`, `poso_estim_sir()` and `poso_estim_mcmc()` can now estimate individual PK profiles for multiple endpoints models (eg. PK-PD, parent-metabolite, blood-CSF...), using a different residual error model for each endpoint.
-* `poso_time_cmin()`, `poso_dose_conc()`, `poso_dose_auc()` and `poso_inter_cmin()` now allow you to select the end point of interest for which you want to optimise, provided it is defined in the model.
+* `poso_time_cmin()`, `poso_dose_conc()`, `poso_dose_auc()` and `poso_inter_cmin()` now allow you to select the end point of interest for which you want to optimize, provided it is defined in the model.
 
 ## Documentation
 * `vignette("a_priori_dosing")` illustrates a priori dose selection