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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.