Revert "Merge branch 'dev'"

This reverts commit 5dc4631, reversing
changes made to a22de93.

README.md

@@ -1,19 +1,15 @@
-# MCMCtree forked
+# MCMCtree 4.9j-forked
 
-MCMCtree is a Bayesian MCMC program for estimation of species divergence times using molecular and trait data. MCMCtree was written by Ziheng Yang from University College London. This is a fork of MCMCtree with small modifications suitable for large-scale analyses. Note this version may lag behind Ziheng's official version. The latest version can be found in [PAML's repository](https://github.com/abacus-gene/paml).
+MCMCtree is a Bayesian MCMC program for estimation of species divergence times using molecular and trait data. MCMCtree was written by Ziheng Yang from University College London. This is a fork of MCMCtree version 4.9j with small modifications suitable for large-scale analyses. The latest version can be found in [PAML's repository](https://github.com/abacus-gene/paml).
 
 ## Citation
 
-If you use this version of MCMCtree in your publications, please indicate this as "MCMCtree forked version [VERSIONNUMBER] available at https://github.com/dosreislab/mcmctree. 
-
-General citations for MCMCtree are:
+If you use this version of MCMCtree in your publications, please indicate so as MCMCtree version 4.9j-forked (available at https://github.com/dosreislab/mcmctree). General citations for MCMCtree are:
 
 * Yang, Z., and B. Rannala. 2006. **Bayesian estimation of species divergence times under a molecular clock using multiple fossil calibrations with soft bounds**. _Molecular Biology and Evolution_, 23: 212–226.
 * Rannala B, Yang Z. 2007. **Inferring speciation times under an episodic molecular clock**. _Systematic Biology_, 56: 453-466. 
 
-The first paper describes the MCMC algorith and the birth-death prior conditioned on soft fossil calibrations. The second paper describes the relaxed clock implementation.
-
-If you use the approximate likelihood method please cite:
+The first paper describes the MCMC algorith and the birth-death prior conditioned on soft fossil calibrations. The second paper describes the relaxed clock implementation. If you use the approximate likelihood method please cite:
 
 * dos Reis M, Yang Z. 2011. **Approximate likelihood calculation for Bayesian estimation of divergence times**. _Molecular Biology and Evolution_, 28:2161-2172.
 
@@ -65,4 +61,4 @@ sampfreq = 6
 nsample = 5000
 ```
 
-**checkpoint** activates checkpointing. If `checkpoint = 1` the state of the MCMC chain will be saved to file `mcmctree.ckpt` every 10% percentile of the total MCMC run length. Burn-in is used as set in the control file and states during burn-in are not saved. If `checkpoint = 2`, the program will resume the MCMC chain from the `mcmctree.ckpt` file. In this case the `burnin` variable is ignored (i.e., is set to zero internally). The state of the chain is also saved every 10% percentile. Note in all cases `mcmctree.ckpt` is overwritten everytime the chain is saved, and thus only the last state saved is available. This version of MCMCtree allows unlimited checkpoint runs, simply rename the `mcmc.txt` file once the run has finished and restart the MCMC chain. The varios MCMC files can then be merged into a larger file and process with `print = -1` in the control file. In short, use `checkpoint = 1` for the first run in which you need to use burn-in, then use `checkpoint = 2` for subsequent runs.
+**checkpoint** activates checkpointing. If `checkpoint = 1` the state of the MCMC chain will be saved to file `mcmctree.ckpt` every 10% percentile of the total MCMC run length. Burn-in is used as set in the control file and states during burn-in are not saved. If `checkpoint = 2`, the program will resume the MCMC chain from the `mcmctree.ckpt` file. In this case the `burnin` variable is ignored (i.e., is set to zero internally). The state of the chain is also saved every 10% percentile. Note in all cases `mcmctree.ckpt` is overwritten everytime the chain is saved, and thus only the last state saved is available. This version of MCMCtree allows unlimited checkpoint runs, simply rename the `mcmc.txt` file once the run has finished and restart the MCMC chain. The varios MCMC files can then be merged into a larger file and process with `print = -1` in the control file. In short, use `checkpoint = 1` for the first run in which you need to use burn-in, then use `checkpoint = 2` for subsequent runs.