-added LG4X model of protein substitution
fixed a bug in the branch length scaler optimization procedure that only occured
on unpartitioned datasets.
fixed a small bug that caused an assertion on optModel() to fail.
sometimes trees were read from a list and their previous likelihood
just stored in tr->likelihood, while the model had changed in the meantime.
Now the likelihood of the restored trees is re-calculated prior to entering
modOpt()
-fixed a bug in an error check that caused an assertion to fail for no reason
-fixed a bug in the optimization procedure that caused an assertion to fail
for the free rates optimization of the LG4X model
incremented version number
yet another bug fix for the LX4 model rate optimization
-added an option for Jiajie
-fixed a faling assertion in the standard tree inference when using a user-defined starting tree
-added the possibility to read-in model parameters and a tree with branch lengths
 as generated by the -f e (RAxML_binaryModelParamaters.RUN_ID) option into the
 Evolutionary placement algorithm via -R

-This will speed up EPA computations in cases were you repeatedly just want to place a
 single sequence into a given reference tree, since the model parameters and branch lengths
 of the reference tree will not need to be re-estimated.

-Current limitations:
 1. does not work with the CAT model
 2. does not work on partitioned datasets
 3. will yield slightly different results than the EPA in standard mode,
    that is without the -R option, but this is actually a thing that can not
    be avoided.

-Example: given a reference data set with 100 taxa 100.phy and a reference tree:
 First optimize model params and branch lengths with -f e:

 ./raxmlHPC-AVX -f e -s 100.phy -m GTRGAMMA -t RAxML_bestTree.T1 -n Evaluate

RAxML will generate a file called: "RAxML_binaryModelParameters.Evaluate"
and a tree file with optimized branch lengths called "RAxML_result.Evaluate"

Then invoke the placement algorithm as follows:

./raxmlHPC-AVX -f v -s 101.phy -m GTRGAMMA -t RAxML_result.Evaluate -R RAxML_binaryModelParameters.Evaluate -n P1

where dataset 101.phy just contains one sequence to be placed via the EPA.

Tests on this single-gene dataset showed that compared to the standard invocation:

./raxmlHPC-AVX -f v -s 101.phy -m GTRGAMMA -t RAxML_result.Evaluate -n P2

This is about 8 times faster in this case.

The actual speedup will depend on the datset size, that is, the larger the reference dataset (100.phy)
the better speedups you will get.
-some general code cleanup
-added the big tree plausibility checking option -f R.
 This option allows to assess the plausibility of a very large tree that can not
 be inspected visually any more by computing its RF distance to many smaller (better quality) trees
 that contain a subset of taxa of the large tree.
 To invoke: ./raxmlHPC-SSE -f R -m GTRCAT -t largeTree -z smallReferenceTrees -n T1
 where file "largeTree" contains the huge tree and "smallReferenceTrees" the smaller
 reference trees against which we want to check.
add ignore and