Pinned Repositories
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Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics
arrow
Apache Arrow is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. It also provides computational libraries and zero-copy streaming messaging and interprocess communication. Languages currently supported include C, C++, Java, JavaScript, Python, and Ruby.
r-helper
Commonly used helper functions in R
RcppPyArrow
Transfer pyarrow data to Rcpp
reticulate
R Interface to Python
sparseinv
Creates a wrapper for the SuiteSparse routines in C that us the Takahashi equations to compute the elements of the inverse of a sparse matrix at locations where the (permuted) Cholesky factor is non-zero. The resulting matrix is known as a sparse inverse subset. Some helper functions (like the permuted Cholesky factorisation) are also implemented. Support for spam matrices is currently limited and will be implemented in the future.
lintr
Static Code Analysis for R
jeffwong-nflx's Repositories
jeffwong-nflx/RcppPyArrow
Transfer pyarrow data to Rcpp
jeffwong-nflx/arrow
Apache Arrow is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. It also provides computational libraries and zero-copy streaming messaging and interprocess communication. Languages currently supported include C, C++, Java, JavaScript, Python, and Ruby.
jeffwong-nflx/r-helper
Commonly used helper functions in R
jeffwong-nflx/reticulate
R Interface to Python
jeffwong-nflx/sparseinv
Creates a wrapper for the SuiteSparse routines in C that us the Takahashi equations to compute the elements of the inverse of a sparse matrix at locations where the (permuted) Cholesky factor is non-zero. The resulting matrix is known as a sparse inverse subset. Some helper functions (like the permuted Cholesky factorisation) are also implemented. Support for spam matrices is currently limited and will be implemented in the future.