diff --git a/examples/python/id-derivatives.py b/examples/python/id-derivatives.py
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+##
+## Copyright (c) 2018 CNRS
+##
+## This file is part of Pinocchio
+## Pinocchio is free software: you can redistribute it
+## and/or modify it under the terms of the GNU Lesser General Public
+## License as published by the Free Software Foundation, either version
+## 3 of the License, or (at your option) any later version.
+##
+## Pinocchio is distributed in the hope that it will be
+## useful, but WITHOUT ANY WARRANTY; without even the implied warranty
+## of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+## General Lesser Public License for more details. You should have
+## received a copy of the GNU Lesser General Public License along with
+## Pinocchio If not, see
+## <http:##www.gnu.org/licenses/>.
+##
+
+import pinocchio as pin
+import numpy as np
+
+##
+## In this short script, we show how to compute the derivatives of the
+## inverse dynamics (RNEA), using the algorithms proposed in:
+##
+## Analytical Derivatives of Rigid Body Dynamics Algorithms, Justin Carpentier and Nicolas Mansard, Robotics: Science and Systems, 2018
+##
+
+# Create model and data
+
+model = pin.Model.BuildHumanoidSimple()
+data = model.createData()
+
+# Set bounds (by default they are undefinded for a the Simple Humanoid model)
+
+model.lowerPositionLimit = -np.matrix(np.ones((model.nq,1)))
+model.upperPositionLimit = np.matrix(np.ones((model.nq,1)))
+
+q = pin.randomConfiguration(model) # joint configuration
+v = np.matrix(np.random.rand(model.nv,1)) # joint velocity
+a = np.matrix(np.random.rand(model.nv,1)) # joint acceleration
+
+# Evaluate the derivatives
+
+pin.computeRNEADerivatives(model,data,q,v,a)
+
+# Retrieve the derivatives in data
+
+dtau_dq = data.dtau_dq # Derivatives of the ID w.r.t. the joint config vector
+dtau_dv = data.dtau_dv # Derivatives of the ID w.r.t. the joint velocity vector
+dtau_da = data.M # Derivatives of the ID w.r.t. the joint acceleration vector