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simil()
nad dist()
to perform
masked similarity/distance computation. If a pattern matrix is given via
the mask
argument, it computes scores for selected pairs of
rows or columns. Pattern matrices can be created using newly added
mask()
function.simil()
. As a result, computation of these
scores became as fast as “cosine”, “correlation” and “euclidean”.crossprod()
and tcrossprod()
using the
same infrastructure as simil()
and
dist()
.sparse = FALSE
to save space
in RAM.options(proxyC.threads)
to control the number of
threads in parallel computing (but
RCPP_PARALLEL_NUM_THREADS
still has effect).simil()
for Fuzzy Jaccard similarity
(#42).use_nan = FALSE
will suppress
warnings in simil()
and dist()
.dist()
for Jensen-Shannon divergence as
a symmetric version of Kullback-Leibler divergence.x
and y
are coerced to
dgCMatrix for Matrix v1.4-2.dist()
for Jeffreys divergence. It is
a symmetric version of Kullback-Leibler divergence (#31).rowSds()
, colSds()
,
rowZeros()
and colZeros()
return row or column
names. They also work with both dense and sparse matrices (#28).simil()
to correct
misspelling (#26).simil()
and dist()
work with both dense
and sparse matrices.use_nan = TRUE
can be used not only for correlation but
for all the distance and similarity measures.use_nan = TRUE
, in which case
the computed correlation similarity will be NaN
instead
(#21).diag
argument to compute similarity/distance
only for corresponding rows or columns (#13).smooth
parameter to chisquared and kullback
leibler distances to solve negative values in sparse matrices
(#15).stats::chisq.test()
(#14).drop0 = TRUE
(#17).drop0
argument to address the floating point
precision issue (#10).dist()
(#11).rowSds()
, colSds()
,
rowZeros()
and colZeros()
(#9).x != y
(#4).digits
argument to correct rounding errors in
C++ (#5).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.