v1.1.0 :
 - RAVA-FIRST:
    *Indels are now considered when computed the median adjusted CADD scores in each CADD region
    *Indels can be annotated and analysed using the RAVA-FIRST strategy

v1.0.0 :
 - Analysis using the RAVA-FIRST stratgey by CADD regions:
    *Used RAVA.FIRST() to run the whole RAVA-FIRST strategy
    *Three files with adjusted CADD scores, CADD regions and Functional categories are available at https://lysine.univ-brest.fr/RAVA-FIRST/ and directly downoladed in Ravages repository if RAVA-FIRST functions are used
    *Variants can be annotated into CADD regions and genomic categories (set.CADDregions)
    *Possilibity to annotate variants with the adjusted CADD score and to filter them based on the median observed in each CADD region (filter.adjustedCADD)
    *Possibility to perform burden tests with subscores in the regression to take into account the genomic categories (burden.subscores)

 - Parallelisation of burden on continuous phenotypes

 - get.effect.size replaces get.OR.values in burden() and enables to get the beta estimate for continuous phenotypes

 - Add the possibility to filter genomic regions based on the cumulative MAF (min.cumulative.maf)

 - Simulations: 
    *random.bed.matrix() is now rbm.GRR()
    *rbm.haplos.power() and rbm.GRR.power() are available to directly compute power of CAST, WSS and SKAT on the corresponding simulations
    *CAST power can be computed using theoretical computations in rbm.GRR.power() 

v0.1.5:
 - Fix bugs with tinythreads and RcppParallel in SKAT

v0.1.2:
 - Parallelisation of SKAT on continuous phenotypes

 - Minor bugs corrected :
   - SKAT() with continuous phenotypes can be run only if at least 2 variants in the genomic region
   - Cleaning temporary files in functions called by mclapply to optimise memory usage

 - Checks added in multiple functions to verify the fit between functions and arguments


