diff --git a/DESCRIPTION b/DESCRIPTION
index 393ff1b..abd5295 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: bayesRecon
 Type: Package
-Date: 2023-08-23
+Date: 2023-12-19
 Title: Probabilistic Reconciliation via Conditioning
-Version: 0.1.2
+Version: 0.2.0
 Authors@R: c(person(given = "Dario",
              family = "Azzimonti",
              role = c("aut","cre"),
diff --git a/NEWS.md b/NEWS.md
index 97866c0..4cb856a 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,13 @@
+# bayesRecon 0.2.0
+
+* Vignette "Properties of the reconciled distribution via conditioning"
+
+* Added option in `reconc_BUIS` to pass some base forecast as parameters and some as samples
+
+* Added option in `reconc_BUIS` to input a list of distributions instead of a string.
+
+* Fixed bugs and closed github issues.
+
 # bayesRecon 0.1.2
 
 * Vignette "Probabilistic Reconciliation via Conditioning with bayesRecon".
diff --git a/README.Rmd b/README.Rmd
index d76b0c1..9d3e8cd 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -36,6 +36,8 @@ The main functions are:
  * `reconc_MCMC`: a generic tool for the reconciliation of probabilistic count time series forecasts via Markov Chain Monte Carlo.
  
 ## News
+:boom: [2023-12-19] Added the vignette "Properties of the reconciled distribution via conditioning".
+
 :boom: [2023-08-23] Added the vignette "Probabilistic Reconciliation via Conditioning with bayesRecon". Added the `schaferStrimmer_cov` function.
 
 :boom: [2023-05-26] bayesRecon v0.1.0 is released!
diff --git a/README.md b/README.md
index 10e2d7d..44d789f 100644
--- a/README.md
+++ b/README.md
@@ -33,6 +33,9 @@ The main functions are:
 
 ## News
 
+:boom: \[2023-12-19\] Added the vignette “Properties of the reconciled
+distribution via conditioning”.
+
 :boom: \[2023-08-23\] Added the vignette “Probabilistic Reconciliation
 via Conditioning with bayesRecon”. Added the `schaferStrimmer_cov`
 function.
diff --git a/vignettes/reconciliation_properties.Rmd b/vignettes/reconciliation_properties.Rmd
index 3dc2025..b9c43bf 100644
--- a/vignettes/reconciliation_properties.Rmd
+++ b/vignettes/reconciliation_properties.Rmd
@@ -1,7 +1,7 @@
 ---
 title: "Properties of the reconciled distribution via conditioning"
 author: "Lorenzo Zambon, Giorgio Corani"
-# date: "2023-12-05"
+date: "2023-12-19"
 lang: "en"
 output: rmarkdown::html_vignette
 bibliography: references.bib
@@ -22,8 +22,8 @@ knitr::opts_chunk$set(
 # Introduction
 
 This vignette reproduces the results of the paper *Properties of the reconciled distributions for Gaussian and count forecasts* [@zambon2023properties], 
-accepted by the International Journal of Forecasting for publication.
-We  replicate here the main experiment of the paper,  where we  reconcile  base forecasts constituted by  negative binomial distributions. 
+accepted for publication in the International Journal of Forecasting.
+We  replicate here the main experiment of the paper (see sec. 3, @zambon2023properties)  where we  reconcile  base forecasts constituted by  negative binomial distributions. 
 
 We use the R package `BayesRecon`.
 
@@ -37,10 +37,10 @@ We release a new data set, containing time series of counts of extreme market ev
 in five economic sectors in the period 2005-2018 (3508 trading days). 
 The counts are computed by considering 29 companies included in the Euro Stoxx 50 index 
 and observing if the value of the CDS spread on a given day exceeds the 90-th percentile of its distribution in the last trading year.
-The companies are divided in the following  sectors: Financial (FIN), Information and Communication Technology (ICT), 
+The companies are divided into the following  sectors: Financial (FIN), Information and Communication Technology (ICT), 
 Manufacturing (MFG), Energy (ENG), and Trade (TRD). 
 
-The hierarchy is composed by 5 bottom time series, the daily number of extreme market  
+The hierarchy is composed of 5 bottom time series, the daily number of extreme market  
 events in each sector, and 1 upper time series (the sum of the different sectors). 
 Data are stored in `extr_mkt_events`.