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 The `vector` package [![Build Status](https://github.com/haskell/vector/workflows/CI/badge.svg)](https://github.com/haskell/vector/actions?query=branch%3Amaster)
 ====================
 
-An efficient implementation of `Int`-indexed arrays (both mutable and immutable), with a powerful loop optimisation framework.
+This package includes various modules that will allow you to 
+work with vectors and use an optimisation framework called [*fusion*](#fusion). 
+In this context, vector is an `Int`-indexed array-like data structure with a simpler 
+API that can contain any Haskell value. Additionally, its equivalence 
+to C-style arrays and optimisation via fusion accelerates vector’s 
+performance and makes it a great alternative to list. By installing this 
+package, you’ll be able to work with [boxed, unboxed, storable, and primitive 
+vectors](#vectors-available-in-the-package) as well as their generic interface.
 
-See [`vector` on Hackage](http://hackage.haskell.org/package/vector) for more information.
+
+## Table of Contents
+
+<!-- no toc -->
+- [Tutorial](#tutorial)
+- [Vector vs Array](#vector-vs-array)
+- [Vectors Available in the Package](#vectors-available-in-the-package)
+- [Stream Fusion](#stream-fusion)
+
+## Tutorial
+
+A beginner-friendly tutorial for vectors can be found on 
+[MMHaskell](https://mmhaskell.com/data-structures/vector).
+
+
+If you have already started your adventure with vectors, 
+the tutorial on [Haskell Wiki](https://wiki.haskell.org/Numeric_Haskell:_A_Vector_Tutorial) 
+covers more ground.
+
+## Vector vs Array
+
+Arrays are data structures that can store a multitude of elements 
+and allow immediate access to every one of them. Even though Haskell 
+has a built-in [Data.Array module](https://hackage.haskell.org/package/array-0.5.7.0), 
+arrays might be a bit overwhelming to use due to their complex API. 
+Conversely, vectors incorporate the array’s *O(1)* access to elements 
+with a much friendlier API of lists. Since they allow for framework 
+optimisation via loop fusion, vectors emphasise efficiency and keep 
+a rich interface. Unless you’re confident with arrays, it’s 
+well-advised to use vectors when looking for a similar functionality.
+
+## Vectors Available in the Package
+
+**Lazy boxed vectors** (`Data.Vector`) store each of their elements as a 
+pointer to a heap-allocated value. Because of indirection, lazy boxed vectors
+are slower in comparison to unboxed vectors.
+
+**Strict boxed vectors** (`Data.Vector.Strict`) contain elements that are 
+[strictly evaluated](https://tech.fpcomplete.com/haskell/tutorial/all-about-strictness/).
+
+**Unboxed vectors** (`Data.Vector.Unboxed`) determine an array's representation
+from its elements' type. For example, vector of primitive types (e.g. `Int`) will be 
+backed by primitive array while vector of product types by structure of arrays.
+They are quite efficient due to the unboxed representation they use.
+
+**Storable vectors** (`Data.Vector.Storable`) are backed by pinned memory, i.e., 
+they cannot be moved by the garbage collector. Their primary use case is C FFI.  
+
+**Primitive vectors** (`Data.Vector.Primitive`) are backed by simple byte array and 
+can store only data types that are represented in memory as a sequence of bytes without
+a pointer, i.e., they belong to the `Prim` type class, e.g., `Int`, `Double`, etc.
+It's advised to use unboxed vectors if you're looking for the performance of primitive vectors,
+but more versality. 
+
+## Stream Fusion
+
+An optimisation framework used by vectors, stream fusion  is a technique that merges 
+several functions into one and prevents creation of intermediate data structures. For example, 
+the expression `sum . filter g . map f` won't allocate temporary vectors if 
+compiled with optimisations.