Update dontrun section
An attempt at improving getRegionCoverage(). With the derfinder example data it doens't seem to help
This third version seems a bit better. It has lower duplications too
Tried to improve getRegionCoverage() -- version 3 won by a hair --.  Might have a greater impact on a real data set
regionMatrix() now has an mc.cores argument and uses mcmapply() instead of mapply(). Use with care with large fullCov objects -- have yet to test on a real data set
v0.0.61 Changed regionMatrix() so it can take the result of lapply(data, filterData, returnMean=TRUE) and thus (I believe) reduce the memory peak when using multiple cores. Changed filterData(), loadCoverage() and getRegionCoverage() to match the changes in regionMatrix(). Note that this means that getRegionCoverage() can now take as input a list where each element is produced by loadCoverage() including non-NULL 'cutoff'. Will proceed to test this version.
Update example for regionMatrix() with new use
Change regionMatrix() to reduce the number of duplications when runFilter = FALSE
v0.0.62 Major change: derfinder no longer uses parallel::mclapply() or parallel::mcmapply(). Instead it uses BiocParallel::bplapply() and bpmapply() along with SnowParam() when mc.cores is greater than 1. Using SnowParam() instead of MulticoreParam() which would have been the equivalent means that the memory no longer blows up (exponetially) when mc.cores increases; instead it increases nearly linearly. It also means that derfinder no longer chokes with the 'long vectors not yet implemented' issue with using mclapply(): at least one previous reproducible case no longer throws this error.
update don't run example for fullCoverage()
Change to alphabetical order
v0.0.63 Now fstats.apply() has three methods: regular, Rle, and Matrix (default). The newer methods are either faster or less memory intensive, although which one of the new methods to use depends on the size of the data set. The old method (regular) is there for scenarios where the projection matrix does not have row sums equal to 0 (for example, when the model matrices do not include an intercept) and for comparison purposes. Several functions and manual pages were updated accordingly.
v0.0.64 fullCoverage() is a full parallel implementation of loadCoverage() now that fullCoverage() doens't blow up in memory usage. fullCoverage() is now the recommended function to use instead of 1 job per chr for loadCoverage() or using lapply(loadCoverage)
Merge pull request #24 from lcolladotor/devel

Memory improvements to all parallel functions, new methods for F-stats calculation, recommended fullCoverage() and improved regionMatrix()