diff --git a/Project.toml b/Project.toml
index 72cf559..90d1330 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "ExpectationMaximization"
 uuid = "e1fe09cc-5134-44c2-a941-50f4cd97986a"
 authors = ["David Métivier <46794064+dmetivie@users.noreply.github.com> and contributors"]
-version = "0.1.5"
+version = "0.1.6"
 
 [deps]
 ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"
diff --git a/README.md b/README.md
index 7a944a1..0019598 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,29 @@
 # ExpectationMaximization
 
 This package provides a simple implementation of the Expectation Maximization algorithm used to fit mixture models.
-Due to [Julia](https://julialang.org/) amazing [multiple dispatch](https://www.youtube.com/watch?v=kc9HwsxE1OY) systems and the [Distributions](https://juliastats.org/Distributions.jl/stable/) package, the code is very generic i.e., mixture of all common distributions should be supported.
+Due to [Julia](https://julialang.org/) amazing [multiple dispatch](https://www.youtube.com/watch?v=kc9HwsxE1OY) systems and the [Distributions](https://juliastats.org/Distributions.jl/stable/) package, the code is very generic i.e., a lot of mixture should work:
 
-I plan to add different methods for E-step and M-steps like stochastic EM and others.
+- Univariate continuous distributions
+- Univariate discrete distributions
+- Multivariate distributions (continuous or discrete).
+- Mixture of mixture (univariate or multivariate and continuous or discrete). Note that [Distributions](https://juliastats.org/Distributions.jl/stable/) currently does not allow `MixtureModel` to have discrete and continuous components (but who does that? Rain).
 
-I should add examples with MNIST dataset and Bernoulli mixtures. Look at the tests to see examples with multivariate distributions.
+**Have a look at the tests sections to see examples**.
+
+To work, the only requirements are that the `dist<:Distribution` considered has implanted
+
+1. `logpdf(dist, y)` (used in the E-step)
+2. `fit_mle(dist, y, weigths)` (used in the M-step)
+
+In general 1. is easy, while 2. is only known explicitly for a few common distributions.
+In case 2. is not explicit known, you can always implement a numerical scheme, if it exists, for `fit_mle(dist, y)` see [`Gamma` distribution example](https://github.com/JuliaStats/Distributions.jl/blob/34a05d8a1671052624e7fa246b58484acc32cfe5/src/univariate/continuous/gamma.jl#L171).
+Or, when possible, represent your “difficult” distribution as a mixture of simple terms.
+(I had [this](https://stats.stackexchange.com/questions/63647/estimating-parameters-of-students-t-distribution) in mind, but it is not directly a mixture model.)
+
+## TODO (feel free to contribute)
+
+- Add different variants for E-step and M-steps like stochastic EM and others.
+- Add examples with MNIST dataset and Bernoulli mixtures.
 
 ## Example
 
@@ -49,4 +67,5 @@ isapprox(θ₁, p[1]...; rtol = rtol)
 isapprox(α, p[2][1]; rtol = rtol)
 isapprox(θ₂, p[2][2]; rtol = rtol)
 ```
+
 ![EM_mixture_example.svg](img/EM_mixture_example.svg)