Add docs for Arrow

docs/src/main/resources/microsite/data/menu.yml

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     url: typeclasses/show.html
     menu_type: typeclasses
 
+  - title: Arrow
+    url: typeclasses/arrow.html
+    menu_type: typeclasses
+
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   # Data Types Menu Options #
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docs/src/main/tut/typeclasses/arrow.md

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+---
+layout: docs
+title:  "Arrow"
+section: "typeclasses"
+source: "core/src/main/scala/cats/arrow/Arrow.scala"
+scaladoc: "#cats.arrow.Arrow"
+---
+# Arrow
+
+`Arrow` is a useful type class for modeling something that behaves like functions, such as `Function1`: `A => B`, `Kleisli`: `A => F[B]` (also known as `ReaderT`), `Cokleisli`: `F[A] => B`, etc. So useful, that Haskell provides special syntax (the `proc` notation) for composing and combining arrows, similar to the `do` notation for sequencing monadic operations.
+
+To create an `Arrow` instance for a type `F[A, B]`, the following abstract methods need to be implemented:
+
+```
+def lift[A, B](f: A => B): F[A, B]
+
+def id[A]: F[A, A]
+
+def compose[A, B, C](f: F[B, C], g: F[A, B]): F[A, C]
+
+def first[A, B, C](fa: F[A, B]): F[(A, C), (B, C)]
+```
+
+Once a type `F` has an `Arrow` instance, it gets for free a number of methods for composing and combining multiple `Arrow`s. You will be able to do things like:
+- Composing `fab: F[A, B]` and `fbc: F[B, C]` into `fac: F[A, C]` with `fab >>> fbc`. If `F` is `Function1` then `>>>` becomes an alias for `andThen`.
+- Taking two arrows `fab: F[A, B]` and `fbc: F[B, C]` and combining them into `F[(A, C) => (B, D)]` with `fab *** fbc`. The resulting arrow takes two inputs and processes them with two arrows, one for each input.
+- Taking two arrows `fab: F[A, B]` and `fac: F[A, C]` and combining them into `F[A => (B, C)]` with `fab &&& fbc`. The resulting arrow takes an input, duplicates it and processes each copy with a different arrow.
+- Taking an arrow `fab: F[A, B]` and turning it into `F[(C, A), (C, B)]` with `fab.second`. The resulting arrow takes two inputs, processes the second input and leaves the first input as it is.
+
+## Example
+
+As an example, let's create a fancy version of `Function1` called `FancyFunction`, that is capable of maintaining states. We then create an `Arrow` instance for `FancyFunction` and use it to compute the moving average of a list of numbers.
+
+```tut:book:silent
+case class FancyFunction[A, B](run: A => (FancyFunction[A, B], B))
+```
+
+That is, given an `A`, it not only returns a `B`, but also returns a new `FancyFunction[A, B]`. This sounds similar to the `State` monad (which returns a result and a new `State` from an initial `State`), and indeed, `FancyFunction` can be used to perform stateful transformations. For example, to run a stateful computation using a `FancyFunction` on a list of inputs, and collect the output into another list, we can define the following `runList` helper function:
+
+```tut:book:silent
+def runList[A, B](ff: FancyFunction[A, B], as: List[A]): List[B] = as match {
+  case h :: t =>
+    val (ff2, b) = ff.run(h)
+    b :: runList(ff2, t)
+  case _ => List()
+}
+```
+
+In `runList`, the head element in `List[A]` is fed to `ff`, and each subsequent element is fed to a `FancyFunction` which is generated by running the previous `FancyFunction` on the previous element. If we have an `as: List[Int]`, and an `avg: FancyFunction[Int, Double]` which takes an integer and computes the average of all integers it has seen so far, we can call `runList(avg, as)` to get the list of moving average of `as`.
+
+Next let's create an `Arrow` instance for `FancyFunction` and see how to implement the `avg` arrow.
+
+
+```tut:book:silent
+import cats.arrow.Arrow
+
+implicit val arrowInstance: Arrow[FancyFunction] = new Arrow[FancyFunction] {
+
+  override def lift[A, B](f: A => B): FancyFunction[A, B] = FancyFunction(lift(f) -> f(_))
+
+  override def first[A, B, C](fa: FancyFunction[A, B]): FancyFunction[(A, C), (B, C)] = FancyFunction {case (a, c) =>
+    val (fa2, b) = fa.run(a)
+    (first(fa2), (b, c))
+  }
+
+  override def id[A]: FancyFunction[A, A] = FancyFunction(id -> _)
+
+  override def compose[A, B, C](f: FancyFunction[B, C], g: FancyFunction[A, B]): FancyFunction[A, C] = FancyFunction {a =>
+    val (gg, b) = g.run(a)
+    val (ff, c) = f.run(b)
+    (compose(ff, gg), c)
+  }
+}
+
+```
+
+Once we have an `Arrow[FancyFunction]`, we can start to do interesting things with it. First, let's create a method `accum` that returns a `FancyFunction`, which accumulates values fed to it using the accumulation function `f` and the starting value `b`:
+
+```tut:book:silent
+def accum[A, B](b: B)(f: (A, B) => B): FancyFunction[A, B] = FancyFunction {a =>
+  val b2 = f(a, b)
+  (accum(b2)(f), b2)
+}
+```
+
+Now we can try it out:
+
+```tut:book
+runList(accum[Int, Int](0)(_ + _), List(6, 5, 4, 3, 2, 1))
+```
+
+To make the aformentioned `avg` arrow, we need to keep track of both the count and the sum of the numbers we have seen so far. To do so, we will combine several `FancyFunction`s to get the `avg` arrow we want.
+
+We first define arrow `sum` in terms of `accum`, and define arrow `count` by composing `_ => 1` with `sum`:
+
+```tut:book:silent
+import cats.implicits._
+import cats.kernel.Monoid
+
+def sum[A: Monoid]: FancyFunction[A, A] = accum(Monoid[A].empty)(_ |+| _)
+def count[A]: FancyFunction[A, Int] = Arrow[FancyFunction].lift((_: A) => 1) >>> sum
+```
+
+Then we define a `combine` function that takes an input and processes two copies of it with two arrows, and finally, the `avg` arrow in terms of the arrows we have so far:
+
+```tut:book:silent
+def combine[F[_, _]: Arrow, A, B, C](fab: F[A, B], fac: F[A, C]): F[A, (B, C)] =
+  Arrow[F].lift((a: A) => (a, a)) >>> (fab *** fac)
+
+def avg: FancyFunction[Int, Double] =
+  combine(sum[Int], count[Int]) >>> Arrow[FancyFunction].lift{case (x, y) => x.toDouble / y}
+```
+
+And try it out:
+
+```tut:book
+runList(avg, List(1, 10, 100, 1000))
+```