Minor Typos

docs/index.md

@@ -52,6 +52,7 @@ mesa runserver examples/wolf_sheep
 
 For more help on using Mesa, check out the following resources:
 
+- [Mesa Overview]
 - [Mesa Introductory Tutorial]
 - [Mesa Visualization Tutorial]
 - [GitHub Issue Tracker]
@@ -99,7 +100,8 @@ tutorials/adv_tutorial_legacy.ipynb
 [github]: https://github.com/projectmesa/mesa/
 [github issue tracker]: https://github.com/projectmesa/mesa/issues
 [mesa]: https://github.com/projectmesa/mesa/
-[mesa introductory tutorial]: tutorials/intro_tutorial.html
-[mesa visualization tutorial]: tutorials/visualization_tutorial.html
+[mesa overview]: https://mesa.readthedocs.io/en/stable/overview.html
+[mesa introductory tutorial]: https://mesa.readthedocs.io/en/stable/tutorials/intro_tutorial.html
+[mesa visualization tutorial]: https://mesa.readthedocs.io/en/stable/tutorials/visualization_tutorial.html
 [pypi]: https://pypi.python.org/pypi/Mesa/
 [ticket]: https://github.com/projectmesa/mesa/issues

docs/overview.md

@@ -16,9 +16,10 @@ Mesa is modular, meaning that its modeling, analysis and visualization component
 
 Most models consist of one class to represent the model itself; one class (or more) for agents; a scheduler to handle time (what order the agents act in), and possibly a space for the agents to inhabit and move through. These are implemented in Mesa's modeling modules:
 
-- `mesa.Model`, `mesa.Agent`
-- [mesa.time](apis/time.html)
-- [mesa.space](apis/space.html)
+- `mesa.Model`
+- `mesa.Agent`
+- [mesa.time](https://mesa.readthedocs.io/en/stable/apis/time.html)
+- [mesa.space](https://mesa.readthedocs.io/en/stable/apis/space.html)
 
 The skeleton of a model might look like this:
 
@@ -56,16 +57,16 @@ model = MyModel(5)
 model.step()
 ```
 
-You should see agents 0-4, activated in random order. See the [tutorial](tutorials/intro_tutorial.html) or API documentation for more detail on how to add model functionality.
+You should see agents 0-4, activated in random order. See the [tutorial](https://mesa.readthedocs.io/en/stable/tutorials/intro_tutorial.html) or API documentation for more detail on how to add model functionality.
 
 To bootstrap a new model install mesa and run `mesa startproject`
 
 ### Analysis modules
 
 If you're using modeling for research, you'll want a way to collect the data each model run generates. You'll probably also want to run the model multiple times, to see how some output changes with different parameters. Data collection and batch running are implemented in the appropriately-named analysis modules:
 
-- [mesa.datacollection](apis/datacollection.html)
-- [mesa.batchrunner](apis/batchrunner.html)
+- [mesa.datacollection](https://mesa.readthedocs.io/en/stable/apis/datacollection.html)
+- [mesa.batchrunner](https://mesa.readthedocs.io/en/stable/apis/batchrunner.html)
 
 You'd add a data collector to the model like this:
 

docs/packages.md

@@ -60,7 +60,7 @@ The commands above should also work with Anaconda, just replace the `pip` with `
 
 ## Package Development: A "How-to Guide"
 
-The purpose of this section is help you understand, setup, and distribute your Mesa package as quickly as possible. A Mesa package is just a Python package or repo. We just call it a Mesa package, because we are talking about a Python package in the context of Mesa. These instructions assume that you are a little familiar with development, but that you have little knowledge of the packaging process.
+The purpose of this section is to help you understand, setup, and distribute your Mesa package as quickly as possible. A Mesa package is just a Python package or repo. We just call it a Mesa package, because we are talking about a Python package in the context of Mesa. These instructions assume that you are a little familiar with development, but that you have little knowledge of the packaging process.
 
 There are two ways to share a package:
 
@@ -85,7 +85,7 @@ Most likely you created an ABM that has the code that you want to share in it, w
 >
 > 4. [Clone the repo to your computer](https://help.github.com/articles/cloning-a-repository/#platform-linux).
 >
-> 5. Copy your code directory into the repo that you cloned one your computer.
+> 5. Copy your code directory into the repo that you cloned on your computer.
 >
 > 6. Add a requirements.txt file, which lets people know which external Python packages are needed to run the code in your repo. To create a file, run: `pip freeze > requirements.txt`. Note, if you are running Anaconda, you will need to install pip first: `conda install pip`.
 >