New function findExclusions() for identifying
incompatible markers in identification cases.
powerPlot() gains a logical argument
jitter, which can be switched on to avoid
overplotting.
checkPairwise() gains an argument
excludeInbred, which is TRUE by default. This is sensible
since the plot shows estimated kappa coefficients, which are
well-behaved only for pairs of noninbred individuals.
forrel now requires R version 4.1 and recent versions of pedtools and ribd. This allowed many simplifications in code and examples.
Added scales as a suggested package.
The new function ibdEstimate() replaces the previous
IBDestimate() (note the name change). This is a complete
rewrite, which optimises the log-likelihood using a projected gradient
descent algorithm, combined with a version of Armijo line
search.
The function ibdBootstrap() replaces the previous
kappaBootstrap() and deltaBootstrap(), and is
considerably faster. This function implements both parametric and
non-parametric bootstrap, controlled with the method
parameter.
The output of ibdEstimate() now has a class
attribute “ibdEst”, with its own print and subsetting methods.
kinshipLR() now handles linked markers by wrapping
MERLIN.
New functions kappaBootstrap() and
deltaBootstrap() for assessing the uncertainty of pairwise
relatedness estimates.
New function randomPersonEP() handling a common
special case of exclusionPower().
forrel now depends on version 0.9.6 (or later) of pedtools.
Deprecated arguments id.labels and
frametitles in missingPersonPlot() has been
removed.
Implement parallelisation in profileSim().
Partial rewrite of kinshipLR(), including new
argument source.
Added the NorwegianFrequencies dataset, containing
allele frequencies for 35 STR markers.
New function missingPersonLR().
New function checkPairwise() replaces the (long
obsolete) examineKinships().
New functions markerSimParametric() and
profileSimParametric() for simulating marker data for two
individuals with given kappa (or condensed identity)
coefficients.
profileSim(), fix bug resulting in identical seeds
given to each parallel cluster.readFam() now has a parameter Xchrom
which can be used to indicate that the markers included in the file are
on the X chromosome
MPPsims() is more flexible, and allows subsetting of
its output.
powerPlot() is more flexible and allows finer
control of the plot contents
readFam(). It is more robust
now, and fails gracefully in certain situations which cannot currently
be handled (e.g. if the file contains twins).