From 9b0b3cb33db73125d381ffd6cd21aecfe3e172d3 Mon Sep 17 00:00:00 2001 From: Twan Koolen Date: Sun, 23 Jun 2019 20:02:21 -0400 Subject: [PATCH] Set version to 2.1.0 and point to latest stable documentation again. --- Project.toml | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/Project.toml b/Project.toml index f9917034..fb9e0b3f 100644 --- a/Project.toml +++ b/Project.toml @@ -1,6 +1,6 @@ name = "RigidBodyDynamics" uuid = "366cf18f-59d5-5db9-a4de-86a9f6786172" -version = "2.0.0" +version = "2.1.0" [deps] DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" diff --git a/README.md b/README.md index e1a6e3b2..25186dc2 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ RigidBodyDynamics.jl is a rigid body dynamics library in pure Julia. It aims to be **user friendly** and [**performant**](https://github.com/JuliaRobotics/RigidBodyDynamics.jl/blob/master/docs/src/benchmarks.md), but also **generic** in the sense that the algorithms can be called with inputs of any (suitable) scalar types. This means that if fast numeric dynamics evaluations are required, a user can supply `Float64` or `Float32` inputs. However, if symbolic quantities are desired for analysis purposes, they can be obtained by calling the algorithms with e.g. [`SymPy.Sym`](https://github.com/JuliaPy/SymPy.jl) inputs. If gradients are required, e.g. the [`ForwardDiff.Dual`](https://github.com/JuliaDiff/ForwardDiff.jl) type, which implements forward-mode [automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation), can be used. -See the [latest documentation](https://JuliaRobotics.github.io/RigidBodyDynamics.jl/dev) for a list of features, installation instructions, and a quick-start guide. Installation should only take a couple of minutes, including installing Julia itself. The documentation includes various usage examples, starting with a [quickstart guide](http://www.juliarobotics.org/RigidBodyDynamics.jl/dev/generated/1.%20Quickstart%20-%20double%20pendulum/1.%20Quickstart%20-%20double%20pendulum/). These examples are also runnable locally as Jupyter notebooks; see [the readme in the examples directory](https://github.com/JuliaRobotics/RigidBodyDynamics.jl/blob/master/examples/README.md) for instructions. +See the [latest stable documentation](https://JuliaRobotics.github.io/RigidBodyDynamics.jl/stable) for a list of features, installation instructions, and a quick-start guide. Installation should only take a couple of minutes, including installing Julia itself. The documentation includes various usage examples, starting with a [quickstart guide](http://www.juliarobotics.org/RigidBodyDynamics.jl/dev/generated/1.%20Quickstart%20-%20double%20pendulum/1.%20Quickstart%20-%20double%20pendulum/). These examples are also runnable locally as Jupyter notebooks; see [the readme in the examples directory](https://github.com/JuliaRobotics/RigidBodyDynamics.jl/blob/master/examples/README.md) for instructions. ## Related packages