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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.
Health stats visible at Monitor.