@@ -35,6 +35,7 @@ sampling are completely customizable.

 The following learners are implemented:

 * `Learner1D`, for 1D functions `f: ℝ → ℝ^N`,

 * `Learner2D`, for 2D functions `f: ℝ^2 → ℝ^N`,

+* `LearnerND`, for ND functions `f: ℝ^N → ℝ^M`,

 * `AverageLearner`, For stochastic functions where you want to average the result over many evaluations,

 * `IntegratorLearner`, for when you want to intergrate a 1D function `f: ℝ → ℝ`,

 * `BalancingLearner`, for when you want to run several learners at once, selecting the "best" one each time you get more points.