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diff --git a/rmr/docs/compatibility.Rmd b/rmr/docs/compatibility.Rmd
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+# Compatibility testing for rmr 1.3.x
+Please contribute with additional reports. To claim compatibility you need to run `R CMD  check path-to-rmr` successfully.
+As with any new release, testing on additional platforms is under way. If you build your own Hadoop, see [Which Hadoop for rmr](https://github.com/RevolutionAnalytics/RHadoop/wiki/Which-Hadoop-for-rmr).
+
+<table>
+<thead>
+<tr><th>rmr</th><th>Hadoop</th><th>R</th><th>OS</th><th>Compatibility</th><th>Reporter</th></tr>
+</thead>
+<tbody>
+<tr><td>1.3.1</td><td>mr1-cdh4.0.0</td><td>Revolution R Enterprise 6.0</td><td>64-bit CentOS 5.6</td><td>only x86_64 and mr1</td><td>Revolution Analytics</td></tr>
+<tr><td>1.3.1</td><td>CDH3u4</td><td>Revolution R Enterprise 6.0</td><td>64-bit CentOS 5.6</td><td>only x86_64</td><td>Revolution Analytics</td></tr>
+<tr><td>1.3.1</td><td>Apache Hadoop 1.0.2</td><td>Revolution R Enterprise 6.0</td><td>64-bit CentOS 5.6</td><td>only x86_64</td><td>Revolution Analytics</td></tr>
+</tbody>
+</table>
diff --git a/rmr/docs/compatibility.html b/rmr/docs/compatibility.html
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+
+<title>Compatibility testing for rmr 1.3.x (Current stable)</title>
+
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+</head>
+
+<body>
+<h1>Compatibility testing for rmr 1.3.x (Current stable)</h1>
+
+<p>Please contribute with additional reports. To claim compatibility you need to run <code>R CMD  check path-to-rmr</code> successfully.<br/>
+As with any new release, testing on additional platforms is under way. If you build your own Hadoop, see <a href="https://github.com/RevolutionAnalytics/RHadoop/wiki/Which-Hadoop-for-rmr">Which Hadoop for rmr</a>.</p>
+
+<table>
+<thead>
+<tr><th>rmr</th><th>Hadoop</th><th>R</th><th>OS</th><th>Compatibility</th><th>Reporter</th></tr>
+</thead>
+<tbody>
+<tr><td>1.3.1</td><td>mr1-cdh4.0.0</td><td>Revolution R Enterprise 6.0</td><td>64-bit CentOS 5.6</td><td>only x86_64 and mr1</td><td>Revolution Analytics</td></tr>
+<tr><td>1.3.1</td><td>CDH3u4</td><td>Revolution R Enterprise 6.0</td><td>64-bit CentOS 5.6</td><td>only x86_64</td><td>Revolution Analytics</td></tr>
+<tr><td>1.3.1</td><td>Apache Hadoop 1.0.2</td><td>Revolution R Enterprise 6.0</td><td>64-bit CentOS 5.6</td><td>only x86_64</td><td>Revolution Analytics</td></tr>
+</tbody>
+</table>
+
+</body>
+
+</html>
+
diff --git a/rmr/docs/compatibility.pdf b/rmr/docs/compatibility.pdf
diff --git a/rmr/docs/tutorial.Rmd b/rmr/docs/tutorial.Rmd
@@ -1,9 +1,9 @@
 
-`r read_chunk('../tests/basic-examples.R')`
-`r read_chunk('../tests/wordcount.R')`
-`r read_chunk('../tests/logistic-regression.R')`
-`r read_chunk('../tests/linear-least-squares.R')`
-`r read_chunk('../tests/kmeans.R')`
+`r read_chunk('../pkg/tests/basic-examples.R')`
+`r read_chunk('../pkg/tests/wordcount.R')`
+`r read_chunk('../pkg/tests/logistic-regression.R')`
+`r read_chunk('../pkg/tests/linear-least-squares.R')`
+`r read_chunk('../pkg/tests/kmeans.R')`
 `r opts_chunk$set(echo=TRUE, eval=FALSE, cache=FALSE, tidy=FALSE)`
 
 # Mapreduce in R
@@ -34,7 +34,7 @@ function, which we are not using here, is a regular R function with a few constr
 1. It returns a key value pair as returned by the helper function `keyval`, which takes any two R objects as arguments; you can also return a list of such objects, or `NULL`.
 
 In this example, we are not using the key at all, only the value, but we still need both to support the general mapreduce case. 
-The return value is an object, and you can pass it as input to other jobs or read it into memory (watch out, not good for big data) with `from.dfs`. `from.dfs` is complementary `to.dfs`. It returns a list of key-value pairs, which is the most general data type that mapreduce can handle. If you prefer data frames to lists, you can instruct `from.dfs` to perform a conversion to data frames, which will cover many many important use cases but is not as general as a list of pairs (structured vs. unstructured case). `from.dfs` is useful in defining map reduce algorithms whenever a mapreduce job produces something of reasonable size, like a summary, that can fit in memory and needs to be inspected to decide on the next steps, or to visualize it.
+The return value is an object, and you can pass it as input to other jobs or read it into memory (watch out, not good for big data) with `from.dfs`. `from.dfs` is complementary `to.dfs`. It returns a list of key-value pairs, which is the most general data type that mapreduce can handle. `from.dfs` is useful in defining map reduce algorithms whenever a mapreduce job produces something of reasonable size, like a summary, that can fit in memory and needs to be inspected to decide on the next steps, or to visualize it.
 
 ## My second mapreduce job