Fixed a bug in my use of
testthat::expect_equal(). See 21 Jan 2022 R-devel/NEWS where it states:
all.equal.numeric()gains a sanity check on its
toleranceargument - calling
all.equal(a, b, c)for three numeric vectors is a surprisingly common error.
multidog()). This protects against some poor behavior observed in a corner case. Specifically, F1 populations where the offspring are all the same genotype and is sequenced at moderate to low depth.
multidog()is now handled by the
futurepackage. If you use the
multidog(), it should still run in parallel using multiple R sessions on your local machine. However, you can now use the functionality of
futureto choose your own evaluation strategy, after setting
nc = NA. This will also allow you to use schedulers in high performance computing environments through the
future.batchtoolspackage. See the
multidog()function documentation for more details.
export_vcf(), is in the works to export
multidogobjects to a VCF file. This is not yet exported because I still have a few bugs to fix.
plot.multidog()will now plot the parent read-counts in F1 and S1 populations.
multidog()now uses iterators through the
iteratorspackage to send only subsets of the data to each R process.
.combinefunction is used in the
multidog()in order to decrease the memory usage of
This is a massive edit of the updog software. Major changes include:
model = "ash". It seemed that
model = "norm"was always better and faster, so I just got rid of the
"ash"option. This also extremely simplified the code.
mupdog(). I think this was a good idea, but the computation was way too slow to be usable.
model = "f1pp"and
model = "s1pp". These now include interpretable parameterizations that are meant to be identified via another R package. But support is only for tetraploids right now.
multidog()now prints some nice ASCII art when it's run.
format_multidog()now allows you to format multiple variables in terms of a multidimensional array.
format_multidog()was reordering the SNP dimensions. This was fine as long as folks used dimnames properly, but now it should allow folks to also use dim positions.
filter_snp()for filtering the output of
multidog()based on predicates in terms of the variables in
multidog()for genotyping multiple SNPs using parallel computing.
plot.multidog()for plotting the output of
format_multidog()for formatting the output of
multidog()to be a matrix.
plot_geno()based on what genotypes are present.
mean_od = 0and
var_od = Infin
method = "custom"option to
flexdog(). This lets users choose the genotype distribution if it is completely known a priori.
model = "s1pp"in
flexdog(). I was originally not constraining the levels of preferential pairing to be the same in both segregations of the same parent. This is now fixed. But the downside is that
model = "s1pp"is now only supported for
ploidy = 4or
ploidy = 6. This is because the optimization becomes more difficult for larger ploidy levels.
I fixed some documentation. Perhaps the biggest error comes from
this snippet from the original documentation of
The value of
prop_misis a very intuitive measure for the quality of the SNP.
prop_misis the posterior proportion of individuals mis-genotyped. So if you want only SNPS that accurately genotype, say, 95% of the individuals, you could discard all SNPs with a
This now says
The value of prop_mis is a very intuitive measure for the quality of the SNP. prop_mis is the posterior proportion of individuals mis-genotyped. So if you want only SNPS that accurately genotype, say, 95% of the individuals, you could discard all SNPs with a prop_mis over 0.05. - I've now exported some C++ functions that I think are useful. You can call them in the usual way.
updog. The old version may be found in the
mupdog()is now live. We provide no guarantees about
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