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