diff --git a/DESCRIPTION b/DESCRIPTION
index 626d30d..d203a96 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: miceFast
 Title: Fast Imputations Using 'Rcpp' and 'Armadillo'
-Version: 0.8.5.9000
+Version: 0.9.0
 Authors@R: person("Maciej", "Nasinski", email = "nasinski.maciej@gmail.com", role = c("aut", "cre"))
 Description: 
   Fast imputations under the object-oriented programming paradigm. 	
diff --git a/NEWS.md b/NEWS.md
index 7e39eee..3aa0b26 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# miceFast 0.8.5.9000
+# miceFast 0.9.0
 
 Kota Hattori, thank you for your feedback and for motivating me for this deep update.
 
diff --git a/README.md b/README.md
index fc563d8..b073f58 100644
--- a/README.md
+++ b/README.md
@@ -127,7 +127,7 @@ See the [Introduction vignette](https://polkas.github.io/miceFast/articles/miceF
 - **Object-Oriented Interface** via `miceFast` objects (Rcpp modules).
 - **Convenient Helpers**:  
   - `fill_NA()`: Single imputation (`lda`, `lm_pred`, `lm_bayes`, `lm_noise`).  
-  - `fill_NA_N()`: Multiple imputations (`pmm`, `lm_bayes`, `lm_noise`).  
+  - `fill_NA_N()`: Averaged multiple imputations (mean of N draws) (`pmm`, `lm_bayes`, `lm_noise`).  
   - `pool()`: Pool multiply imputed results using Rubin's rules.  
   - `VIF()`: Variance Inflation Factor calculations.  
   - `naive_fill_NA()`: Automatic naive imputations.  
@@ -151,6 +151,7 @@ See the [Introduction vignette](https://polkas.github.io/miceFast/articles/miceF
 
 ## Practical Advice
 
+- **Only need a filled-in dataset for exploration or ML?** A single imputation with `fill_NA()` or averaging draws with `fill_NA_N()` is fast and convenient. For any inferential statement use full MI with `pool()`.
 - **Little missing data + MCAR?** Consider using `complete.cases()` — listwise deletion is unbiased under MCAR and may be sufficient when the fraction of incomplete rows is small.
 - **For publication**, always run a **sensitivity analysis**: compare MI results against base methods (`complete.cases()`, mean imputation) and across different imputation models (`lm_bayes`, `lm_noise`, `pmm`). Vary the number of imputations. If conclusions change, investigate why. Report the imputation model, *m*, and any assumptions about the missing-data mechanism.
 - See the [MI vignette](https://polkas.github.io/miceFast/articles/missing-data-and-imputation.html) for details on MCAR/MAR/MNAR mechanisms and a practical checklist.