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Release 1.0.3
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A few minor changes in the S4 classes used:

1. The class constraintGroup has been removed. Simple lists of constraintSet objects are used in its place.
2. The class postProbs has been removed. In its place, the slot probs in objects of class cosmo now contains a list with each element giving the posterior probability of motif occurrences along a particular sequence. Posterior probabilities are given a negative sign if the motif is more likely to occur in the reverse complement orientation than in the forward strand orientation.
3. The class cosmo now contains a slot seqs that consists of a list of the sequences in the input dataset.
4. The slots conGrp and roc.motifs in class cosmo have been removed.
5. Posterior probability plots are now generated using the plot() method on objects of class cosmo with the argument type="prob", rather than by calling the plot() method on an object of class postProbs.
6. A few other slots in the cosmo object have been renamed: cand.orders -> back, cand.model -> cand, transMats ->tmat, sel.model -> sel.

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Release 1.0.2
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A few minor changes and bug fixes:

1. simScore() now calculates the area under the ROC curve as well. It now takes as input an align object and a cosmo object.
2. rseq() can now simulate sequences of variable lenghts.
3. The cosmo class now includes a slot of class align that ranks all potential start sites by posterior probability.
4. The readFASTA() function (not exported) now closes any connection that was opened during the call.
5. The example file seq.fasta now contains 10 rather than 20 sequences to make the vignette less bulky.
6. The package now uses a CITATION file instead of the citation.cosmo() function.
7. Global variables created by the constraint GUI are removed on exit.
8. A minor bug in the selection of the order of the background model was fixed. The bug occurred with some datasets that contained unknown characters like 'N' or 'X'.

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Release 1.0.1
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A few minor changes and bug fixes:

1. If the datasets contains only one sequence, only the TCM model will be considered. A 0th order Markov model is used for the background distribution. All other model parameters are selected based on the E-value by default.
2. The TCM model does not report overlapping motifs anymore.
3. A minor bug in creating the sel.model output slot was fixed.

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Release 1.0.0
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Initial release.