Updated documentations (typos, etc)

README.md

 # ADerrors.jl
 
-Error propagation and analysis of Monte Carlo data with the (``\Gamma``) method and automatic differentiation in `Julia`
+Error propagation and analysis of Monte Carlo data with the $`\Gamma`$ method and automatic differentiation in `Julia`
 
-The full documentation of the package is available via the usual
-[Julia `REPL` help
-mode](https://docs.julialang.org/en/v1/stdlib/REPL/#Help-mode-1) and
-online in [HTML format](https://ific.uv.es/~alramos/docs/ADerrors/).
+- [Features](#features)
+- [Installation](#installation)
+- [Tutorial](#tutorial)
+- [Full documentation](#full-documentation)
+- [Performance tips](#performance-tips)
+- [How to cite](#how-to-cite)
 
-This work is an implementation of several ideas in data analysis. If you use this package for your scientific work, please consider citing:
-- U. Wolff, "Monte Carlo errors with less errors".
-  Comput.Phys.Commun. 156 (2004) 143-153. DOI: 10.1016/S0010-4655(03)00467-3
-- F. Virotta, "Critical slowing down and error analysis of lattice QCD simulations." PhD thesis.
-- Stefan Schaefer, Rainer Sommer, Francesco Virotta, "Critical slowing
-  down and error analysis in lattice QCD simulations". Nucl.Phys.B 845 (2011) 93-119.
-- A. Ramos, "Automatic differentiation for error analysis of Monte Carlo data". Comput.Phys.Commun. 238 (2019) 19-35. DOI: 10.1016/j.cpc.2018.12.020. 
-- M. Bruno, R. Sommer, In preparation.
+## Features
+
+- **Exact** linear error propagation, even in iterative algorithms.
+- **Exact** linear error propagation in **fit parameters**,
+  **integrals** and **roots** of non linear functions. 
+- Handles data from **any number of ensembles** (i.e. simulations with
+  different parameters).
+- Support for **replicas** (i.e. several runs with the same simulation
+  parameters). 
+- **Irrelgular MC measurements** are handled transparently.
 
 ## Installation
 
-The package in not in the general registry. Still one can use the package manager
+The package in not in the general registry. Still one can use the
+package manager. `ADerrors.jl` also depends on `BDIO.jl` that is also
+not registered and should be installed beforehand:
 ```julia
 julia> import Pkg
+(v1.1) pkg> add https://gitlab.ift.uam-csic.es/alberto/bdio.jl
 (v1.1) pkg> add https://gitlab.ift.uam-csic.es/alberto/aderrors.jl
 ```
-## Features
+## Tutorial
 
-- **Exact** linear error propagation, even in iterative algorithms
-  (i.e. error propagation in fit parameters).
-- Handles data from **any number of ensembles** (i.e. simulations with
-  different parameters).
-- Support for **replicas** (i.e. several runs with the same simulation
-  parameters). 
-- Irrelagular MC measurements are handled transparently.
+Please, have a look at the [Getting started](https://ific.uv.es/~alramos/docs/ADerrors/tutorial/) guide.
 
-## Tutorial
+## Full documentation
 
-It is better to start with the [Getting started](https://ific.uv.es/~alramos/docs/ADerrors/tutorial/) guide.
+The full documentation of the package is available via the usual
+[Julia `REPL` help
+mode](https://docs.julialang.org/en/v1/stdlib/REPL/#Help-mode-1) and
+online in [HTML format](https://ific.uv.es/~alramos/docs/ADerrors/).
 
-# Alleviating time to first run
+## Performance tips
 
-`Julia` is well known for being slow the first time that you run some
-routines. On the first call to a function Julia not only runs the
+`Julia` is well known for being slow the first time that you call a
+function. This is because `Julia` not only runs the 
 code, but also compiles it, making the first call slow.
 This problem can be alleviated in general with
 [PackageCompiler.jl](https://github.com/JuliaLang/PackageCompiler.jl). This
@@ -54,14 +58,14 @@ annotate the functions that are compiled
 julia --trace-compile=precompile_aderrors.jl typical.jl
 ```
 Now the functions annotated in `precompile_aderrors.jl` can be
-compiled and included in a `sysimage` that is autmatically loaded
+compiled and included in a `sysimage` that is automatically loaded
 whenever you start Julia
 ```julia
 julia> using PackageCompiler
 julia> PackageCompiler.create_sysimage(:ADerrors; precompile_statements_file="precompile_aderrors.jl", replace_default=true)
 ```
 
-This will make `ADerrors` from the first call. Obviously you can tune
+This will make `ADerrors` fast from the first call. Obviously you can tune
 the file `typical.jl` to your usage, or add other packages. Please
 note that packages included in the sysimage are locked to the versions
 of the sysimage. If you update `ADerrors` make sure to re-generate the
@@ -69,5 +73,16 @@ sysimage. Probably is better to read [the documentation of
 PackageCompiler](https://julialang.github.io/PackageCompiler.jl/dev/sysimages/)
 in order to fully understand the drawbacks.
 
+## How to cite
+
+This work is an implementation of several ideas in data analysis. If you use this package for your scientific work, please consider citing:
+- U. Wolff, "Monte Carlo errors with less errors".
+  Comput.Phys.Commun. 156 (2004) 143-153. 
+- F. Virotta, "Critical slowing down and error analysis of lattice QCD simulations." PhD thesis.
+- Stefan Schaefer, Rainer Sommer, Francesco Virotta, "Critical slowing
+  down and error analysis in lattice QCD simulations". Nucl.Phys.B 845 (2011) 93-119.
+- A. Ramos, "Automatic differentiation for error analysis of Monte Carlo data". Comput.Phys.Commun. 238 (2019) 19-35. 
+- M. Bruno, R. Sommer, In preparation.
+
 
 

docs/src/api.md

 # API
 
-
 ## Creating `uwerr` data types
 
 ```@docs