Changes in version 0.1.5                        

Performance

  - RowMergeMatrices() scatters CSR rows straight into CSC storage.
  - ReplaceColsC() copies unchanged columns and splices replacements in
    one pass.
  - IntegrateDataC() multiplies anchor weights with a dense anchor
    buffer instead of a generic sparse matrix product.
  - fast_dist() writes distances straight into R vectors. Inputs of a
    few thousand cells stay in column-major order; larger inputs are
    transposed and computed in parallel.
  - WriteEdgeFile() and DirectSNNToFile() format edge weights into one
    buffer. The file text is unchanged. DirectSNNToFile() uses the
    parallel SNN kernel from 256 cells, so the 400-cell case is
    parallel.
  - Returned dgCMatrix objects are filled by assigning S4 slots. That
    skips the methods::new() validity scan on CSC data that is already
    valid.

                        Changes in version 0.1.4                        

Performance

  - FastExpMean(), FastLogVMR(), SparseRowVar(), and SparseRowVarStd()
    scan CSC columns directly and accumulate per gene. They no longer
    build a row index first, which made them slower than Seurat.

                        Changes in version 0.1.3                        

Seurat compatibility fixes

  - fast_dist() accepts integer neighbor ids (previously it panicked
    unless ids were doubles), keeps the names of the neighbor list like
    Seurat, and reports out-of-range or NA ids as R errors.
  - GraphToNeighborHelper() now reads each cell's neighbors from its
    row, as Seurat does; it previously read columns, which gave
    different neighbor sets for asymmetric graphs. Ragged rows raise an
    R error instead of a panic.
  - DirectSNNToFile() returns the SNN graph as a dgCMatrix (it returned
    the raw x/i/p/Dim list) and validates nn_ranked like ComputeSNN().
  - DirectSNNToFile() and WriteEdgeFile() format edge weights like C++
    setprecision(15), so edge files are byte-identical to Seurat's. File
    errors are R errors instead of panics.
  - Integer matrices and vectors are coerced to double before reaching
    Rust wherever Seurat's Rcpp signature would coerce them
    (ComputeSNN(), FastCov(), SparseRowVar2() means, and others).
  - RowMergeMatrices() still accepts a dgCMatrix (Seurat requires an
    RsparseMatrix); this is now documented.

Performance

  - FastCov() and FastCovMats() read R memory directly and use a
    blocked, multi-threaded matrix product (FastCov() only computes the
    upper triangle). They were several times slower than Seurat and are
    now faster.
  - FastRBind() copies columns straight into the result instead of going
    through element-wise ndarray conversions (about 50x faster
    than 0.1.2 on large inputs).

Build

  - Removed the unused Eigen C++ bridge. Source builds no longer need a
    C++ compiler or RcppEigen, which fixes builds where c++ is Clang
    without a usable C++ standard library ('cmath' file not found).

                        Changes in version 0.1.2                        

  - Update maintainer contact for CRAN submissions.
  - Link the Rust static library with Apple ld (-force_load) so macOS
    source builds succeed.
  - Carry forward the 0.1.1 packaging fixes for Windows configure and
    portable Makevars.

                        Changes in version 0.1.1                        

  - Fix Windows source installs for CRAN by adding configure.win /
    cleanup.win so Makevars.win is generated and the Rust static library
    is linked.
  - Remove non-portable .NOTPARALLEL from Makevars templates.
  - Clarify DESCRIPTION / README wording for CRAN incoming checks.

                        Changes in version 0.1.0                        

  - Initial public release of Rust/extendr native kernels matching
    Seurat's internal C++ API (LogNorm, FastSparseRowScale, ComputeSNN,
    IntegrateDataC, RunModularityClusteringCpp, and related helpers).
  - Offline CRAN builds via vendored Rust crates
    (src/rust/vendor.tar.xz).
  - Installable from GitHub Releases, r-universe, and (after acceptance)
    CRAN.