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NEWS

• Update tests to pass CRAN check with Intel MKL.

• Add workaround for a compilation error when clang 20 is used (#65).

• Enable 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.
• Calculate “dice” and “edice” using Armadillo’s linear algebra functions in simil(). As a result, computation of these scores became as fast as “cosine”, “correlation” and “euclidean”.
• Add crossprod() and tcrossprod() using the same infrastructure as simil() and dist().

• Reduce the overhead for dense similarity matrices by improving rounding numbers and conversion to Rcpp vectors.
• Return a dense matrix if sparse = FALSE to save space in RAM.

• Make detection of Intel oneAPI Threads Building Blocks (TBB) library more reliable.

• Use more recent Intel oneAPI Threads Building Blocks (TBB) library to improve the stability in parallel computing.
• Add options(proxyC.threads) to control the number of threads in parallel computing (but RCPP_PARALLEL_NUM_THREADS still has effect).

• The RcppParallel package is no longer required as the TBB library in the operating system (Linux and MacOS) or Rtools (Windows) is used.
• Linux and MacOS must have the TBB library to enable parallel computing before installing this package from the source.

• Add “fjaccard” to simil() for Fuzzy Jaccard similarity (#42).

• Explicitly setting use_nan = FALSE will suppress warnings in simil() and dist().
• Add vignettes to explain how the similarity and distance measures are computed.

• Make further changes for Matrix v1.4-2.

• Add “jensen” to dist() for Jensen-Shannon divergence as a symmetric version of Kullback-Leibler divergence.
• Change how x and y are coerced to dgCMatrix for Matrix v1.4-2.

• Add “jeffreys” to 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).

• Change “hamman” to “hamann” in 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.

• Computing the correlation similarity on vectors with a standard deviation will generate a zero correlation and a warning. The warning can be turned off by setting use_nan = TRUE, in which case the computed correlation similarity will be NaN instead (#21).

• Fixed infinite values being returned by the correlation similarity (#20).

• Added a diag argument to compute similarity/distance only for corresponding rows or columns (#13).
• Added a smooth parameter to chisquared and kullback leibler distances to solve negative values in sparse matrices (#15).
• Added the hamming distance (#18)

• Fixed the chi-squared distance to match stats::chisq.test() (#14).
• Fixed a bug in pairwise similarity/distance computation when drop0 = TRUE (#17).

• Add the drop0 argument to address the floating point precision issue (#10).

• The digit argument is now passed to dist() (#11).

• Add rowSds(), colSds(), rowZeros() and colZeros() (#9).

• No longer assumes symmetry of resulting matrix when x != y (#4).

• Add the digits argument to correct rounding errors in C++ (#5).

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