Small fixes to readme and dependencies

DESCRIPTION

@@ -12,6 +12,8 @@ RoxygenNote: 7.3.3
 URL: https://github.com/treeppl/treepplr,
     http://treeppl.org/treepplr/
 BugReports: https://github.com/treeppl/treepplr/issues
+Depends:
+    R (>= 4.1.0)
 Imports: 
     assertthat,
     methods,

README.md

@@ -2,20 +2,28 @@
 
 ## Overview
 
-treepplr is the R Interface to [TreePPL](treeppl.org), a probabilistic programming language for phylogenetics.
+treepplr is the R Interface to [TreePPL](https://treeppl.org), a probabilistic programming language for phylogenetics.
 
-treepplr converts data to a format readable by TreePPL, reads the TreePPL output, and connects to downstream analyses in model-specific packages, such as the [evolnets](github.com/maribraga/evolnets) package for the host repertoire evolution model.
+treepplr converts data to a format readable by TreePPL, reads the TreePPL output, 
+and connects to downstream analyses in model-specific packages, such as the 
+[evolnets](https://github.com/maribraga/evolnets) package for the host repertoire evolution model.
 
-See the package's documentation [here](treeppl.org/treepplr).
+See the package's documentation [here](https://treeppl.org/treepplr). There you 
+will find all package functions under [Reference](https://treeppl.org/treepplr/reference/index.html),
+and basic examples of running various models under [Articles](https://treeppl.org/treepplr/articles/index.html).
 
 
 ## Installation
 
 You can install treepplr like so:
 
 ``` r
-# install.packages("devtools")
-# library(devtools)
+if(!require("devtools", quietly = TRUE)) {
+  install.packages("devtools")
+  library(devtools)
+} else {
+ library(devtools)
+}
 
 devtools::install_github("treeppl/treepplr")
 ```

inst/extdata/hostrep3states.tppl

@@ -1,11 +1,11 @@
-import "host-rep-lib/lib.tppl"
-import "host-rep-lib/container-types.tppl"
-import "host-rep-lib/helpers.tppl"
-import "host-rep-lib/belief-propagation.tppl"
-import "host-rep-lib/dist-helpers.tppl"
-import "host-rep-lib/full-model.tppl"
-import "host-rep-lib/rb-drift-kernels.tppl"
-import "host-rep-lib/independence-model.tppl"
+import "treeppl::models/host-repertoire-evolution/host-rep-lib/lib.tppl"
+import "treeppl::models/host-repertoire-evolution/host-rep-lib/container-types.tppl"
+import "treeppl::models/host-repertoire-evolution/host-rep-lib/helpers.tppl"
+import "treeppl::models/host-repertoire-evolution/host-rep-lib/belief-propagation.tppl"
+import "treeppl::models/host-repertoire-evolution/host-rep-lib/dist-helpers.tppl"
+import "treeppl::models/host-repertoire-evolution/host-rep-lib/full-model.tppl"
+import "treeppl::models/host-repertoire-evolution/host-rep-lib/rb-drift-kernels.tppl"
+import "treeppl::models/host-repertoire-evolution/host-rep-lib/independence-model.tppl"
 
 type ReturnType = ReturnType{
   lambda: Real[], mu: Real, beta: Real, tree: HistoryTree
@@ -54,7 +54,7 @@ model function rejectAccept(
   let leftMsg = mtxMul(newMsg, postorderTree.leftKernel);
   let rightMsg = mtxMul(newMsg, postorderTree.rightKernel);
 
-  // Construct an object containing the rate matrix in a suitable format for sampling 
+  // Construct an object containing the rate matrix in a suitable format for sampling
   // from the embedded Markov chain
 
   let embeddedQMatrix = rateMatrixToEmbeddedMarkovChain(qMatrix);
@@ -84,12 +84,12 @@ model function rejectAccept(
     beta = beta,
     tree = historyTree
   };
-} 
+}
 
 function suggestRepAligned(msg: Matrix[Real], i: Int, max: Int) => Int[] {
   if i <= max {
     let param = mtx3ToSeq(msg, i);
-    assume x ~ Categorical(param) drift categoricalMove(x, param); 
+    assume x ~ Categorical(param) drift categoricalMove(x, param);
     return cons(x, suggestRepAligned(msg, i + 1, max));
   } else {
     return [];
@@ -115,7 +115,7 @@ function suggestRepRS(msg: Matrix[Real], max: Int, initialRep: Int[], depth: Int
 function suggestRepUnaligned(msg: Matrix[Real], i: Int, max: Int) => Int[] {
   if i <= max {
     let param = mtx3ToSeq(msg, i);
-    assume x ~ Categorical(param); 
+    assume x ~ Categorical(param);
     return paste0([[x], suggestRepUnaligned(msg, i + 1, max)]);
   } else {
     return [];
@@ -234,11 +234,11 @@ function sampleBranch(
       let orderedEvents = qSort(compAge, allHostEvents);
       let nEvents = length(orderedEvents);
       if allTimesValidBranch(startRep, orderedEvents, 1, nEvents, nhosts) {
-        // The branch is correct along the full history, 
+        // The branch is correct along the full history,
         // accept and calculate the true weight of the sample
-        
+
         // Calculate the debt -- this is the density of the endpoint conditioned
-        // (parallel) chains at the current sample. 
+        // (parallel) chains at the current sample.
         let logDebt = independenceLikelihoodEndCond(
           startRep, finalRep, startAge, finalAge, unorderedBranch.history, modelParams, branchKernel
         );
@@ -274,17 +274,17 @@ function sampleBranch(
       logDebt = -log(0.0),
       logExcess = log(0.0),
       success = false
-    };   
+    };
   }
 }
 
 function compAge(left: Event, right: Event) => Int {
   if (isNaN(right.eventTime)) {
     return -1;
-  } 
+  }
   if (isNaN(left.eventTime)) {
     return 1;
-  } 
+  }
   if (right.eventTime >= left.eventTime) {
     return 1;
   } else {