Update readme & vignette abstract

README.md

@@ -20,6 +20,12 @@ CLVs in continuous non-contractual business settings such as retailers,
 probabilistic customer attrition models are the preferred choice in
 literature and practice.
 
+Below, we provide broad overview on the functionalites of CLVTools and a quickstart tutorial. More detailed information is provided in the following documents:
+- For more information on the terminology and general modeling challenges when assessing customers' future value look at the vignette ["Probabilistic Models for Analyzing Customer Purchase Behavior: A Primer"](https://cran.r-project.org/web/packages/CLVTools/vignettes/CLVTools_intuitive_explanations.pdf).
+- For a comprehensive case study with CLVTools look at the vignette: ["Walkthrough for the CLVTools Package"](https://cran.r-project.org/web/packages/CLVTools/vignettes/CLVTools.pdf).
+- For advanced modeling techniques look a the vignette ["Advanced and Very Advanced Modeling Techniques in CLVTools"](https://cran.r-project.org/web/packages/CLVTools/vignettes/CLVTools_advanced_techniques.pdf).
+- To understand the internal object-oriented architecture of CLVToools look at the vignette ["Classes in CLVTools"](https://cran.r-project.org/web/packages/CLVTools/vignettes/CLVTools_classes.pdf). 
+
 The R package `CLVTools` provides an efficient and easy to use
 implementation framework for probabilistic customer attrition models in
 non-contractual settings. Building up on the learnings of other

vignettes/CLVTools.Rmd

@@ -11,13 +11,28 @@ output:
     latex_engine: xelatex
     toc: true
     number_sections: yes
+papersize: A4
 bibliography: bibliography.bib
 vignette: >
   %\VignetteIndexEntry{The CLVTools Package} 
   %\VignetteEncoding{UTF-8} 
   %\VignetteEngine{knitr::rmarkdown}
+abstract: |
+  This vignette is a hands-on guide to the R package `CLVTools` for modeling and forecasting
+  customer base dynamics. It shows how to construct `clv.data` objects, estimate latent
+  attrition models (Pareto/NBD, BG/NBD, GGom/NBD), and generate individual-level forecasts:
+  conditional expected transactions (CET), probability of being alive (PAlive), and discounted
+  expected residual (or finite-horizon) transactions (DERT/DECT). We demonstrate the use of
+  time-invariant and time-varying covariates, optional purchase–attrition correlation via a
+  Sarmanov specification, and regularization and equality constraints for covariate effects.
+  The vignette also covers the Gamma/Gamma spending model for predicting mean spend and
+  computing CLV, and provides reproducible code for summaries, diagnostics, and plots.
+  Guidance on data preparation, estimation/holdout splitting, optimizer settings, and result
+  interpretation is included throughout.
 ---
 
+\newpage
+
 
 ```{r setup, include = FALSE}
 knitr::opts_chunk$set(