- New function,
subset_variants, which retains only variants with data bearing upon pathogenicity.
- Return posterior mean of
omega even when not explicitly sampled in
bevimed_polytomous function added which enables application of BeviMed across multiple association models.
BeviMed objects now more general, representing results of inference with respect to the baseline model
gamma = 0 and an arbitrary number of alternative association models - typically, one for each mode of inheritance. The
$moi slot has been replaced with
prob_pathogenic now returns a list when broken down by mode of inheritance/model.
- Make BeviMed work smoothly when number of individuals or number of variants is 0.
- Retain names of variants from columns of original allele count matrix.
- Improvements to guide, with more detail on model selection.
- Fixed bug in calculation of expected number of explaining variants by only including those with pathogenic configurations.
bevimed function now replaced by
bevimed_m, with the
_m indicating that it conditions on mode of inheritance.
bevimed now integrates over indicator of association (gamma) and mode of inheritance (m), allowing user to specify priors on probability of association and probability of dominance.
BeviMed class object has been replaced by
BeviMed_m, and a new
BeviMed class has been introduced for inference with respect to all models: gamma 0 and gamma 1 under each mode of inheritance.
- A new vignette with more detail called
BeviMed Guide which relates the package to the paper.
- Names used for summary statistics in summary objects have changed, see function help pages for details on current names.
BeviMed object now shows conditional probabilities of pathogenicity for each mode of inheritance, and expected explained cases and expected explaining variants shown too.
- Bug fixed in adaptive tuning for omega and phi proposals.
- Re-naming of parameters in
bevimed function to match the names of variables in the paper (under submission).
- The allele count matrix
G should now be supplied as a matrix with rows corresponding to individuals, not variants.
explaining_variants functions have been added, respectively computing the expected number of cases with their disease explained by the given variants, and expected number of pathogenic variants present amongst cases.