updated description

DESCRIPTION

@@ -2,15 +2,15 @@ Package: plsmmLasso
 Title: Variable Selection and Inference for Partial Semiparametric Linear Mixed-Effects Model
 Version: 0.0.0.9000
 Authors@R: 
-    person("Sami", "Leon", , "samileon@hotmail.fr", role = c("aut", "cre"),
+    person("Sami", "Leon", , "samileon@hotmail.fr", role = c("aut", "cre", "cph"),
            comment = c(ORCID = "https://orcid.org/0000-0001-9138-9450"))
-Description: Implements a partial linear mixed-effects model (PLMM) featuring a 
+Description: Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a 
   random intercept and applies a lasso penalty to both the fixed effects and the 
   coefficients associated with the nonlinear function. The model also accommodates
   interactions between the nonlinear function and a grouping variable, allowing
   for the capture of group-specific nonlinearities. Nonlinear functions are 
-  modeled using a set of basis functions. Estimation is conducted using
-  an Expectation-Maximization algorithm, and the package offers flexibility 
+  modeled using a set of bases functions. Estimation is conducted using
+  a penalized Expectation-Maximization algorithm, and the package offers flexibility 
   in choosing between various information criteria for model selection, including 
   BIC, BICC, or EBIC. Post-selection inference is carried out using a debiasing
   method, while inference on the nonlinear functions employs a bootstrap approach.