DART is a very useful toolkit for implementing sophisticated controllers and easily running physics simulations, but a familiarity with control theory and numerical methods is still necessary in order to get sane results. Since the robotics community consists of people from a broad variety of backgrounds, it would be useful for DART to provide some introductory educational tutorials to familiarize

Example:

auto const xml = R"urdf(
    <robot name="test">
        <link name="base">
        </link>
        <link name="link1">
            <inertial>
            <origin xyz="0. 0. 0."/>
            <mass value="1."/>
            <inertia ixx="1." ixy="0." ixz="0." iyy="1." iyz="0." izz="1."/>
            </inertial>
        </link>
        <joint name="joint0" type="continuo

From this page, selecting the "Inverted pendulum model of a standing human" goes to this Jupyter Notebook which is just an intro. Also the link at the end of the page:

![test](https://user-images.githubuser

On SDK documentation it is said that for example with Delete message Target of InputParameters is EntityReference. I've seen on few occasions developers forgetting this when implementing unit tests and they've used Entity type instead of EntityReference.

"Addition" part:
"mutally orthoganl vectors" -> "mutually orthogonal vectors"
"The magnitude of a vector can be found with::" -> "The magnitude of a vector can be found with:"

"Derivatives of vectors" part:
"Here, we have benefitted from the fact that..." -> "Here, we have benefited from the fact that"

The python implementation of leapfrog does not make use of the fact that drifts can be combined when not storing output, causing it to be slower than necessary. Comparing to the C implementation, it also appears to not be as precise.

The docstring of crossprediction, which btw we may want to rename to crossestimation has:

crossprediction(source_train, target_train, source_pred,

yet none of these terms are in the source code for the function , which reads:

function crossprediction(train_in ::AbstractVector{<:AbstractArray{T, Φ}},
                        train_out::AbstractVector{<:AbstractArray{T, Φ}}

Show how to simulate a stream with a perturber.

Things to check (see robotology/idyntree#673 (comment)) :

• DOFs and link serializations
• Velocity, force and acceleration representations for the base quantities
• How we need to transform the vector and matrix input and output quantities appropriately given these differences

Copy pasting the first few lines of the tutorial
https://teneto.readthedocs.io/en/latest/tutorial/networkrepresentation.html#temporalnetwork-object
yields an error at tnet.network.head() because tnet created with
tnet.generatenetwork('rand_binomial',size=(5,3), prob=0.5)
is a Numpy ndarray and not a Pandas DataFrame.

I haven't tried other ways of creating tnet, so I'm not sure if `t