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greta-dev
greta:Simple and Scalable Statistical Modelling in R
Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'. greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it’s easy to extend and build on. See the website for more information, including tutorials, examples, package documentation, and the greta forum.
Maintained by Nicholas Tierney. Last updated 19 days ago.
566 stars 12.53 score 396 scripts 6 dependents
rudjer
SparseM:Sparse Linear Algebra
Some basic linear algebra functionality for sparse matrices is provided: including Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.
Maintained by Roger Koenker. Last updated 9 months ago.
3 stars 11.47 score 306 scripts 1.5k dependents
wrathematics
float:32-Bit Floats
R comes with a suite of utilities for linear algebra with "numeric" (double precision) vectors/matrices. However, sometimes single precision (or less!) is more than enough for a particular task. This package extends R's linear algebra facilities to include 32-bit float (single precision) data. Float vectors/matrices have half the precision of their "numeric"-type counterparts but are generally faster to numerically operate on, for a performance vs accuracy trade-off. The internal representation is an S4 class, which allows us to keep the syntax identical to that of base R's. Interaction between floats and base types for binary operators is generally possible; in these cases, type promotion always defaults to the higher precision. The package ships with copies of the single precision 'BLAS' and 'LAPACK', which are automatically built in the event they are not available on the system.
Maintained by Drew Schmidt. Last updated 20 days ago.
float-matrixhpclinear-algebramatrixfortranopenblasopenmp
46 stars 10.53 score 228 scripts 42 dependents
reinhardfurrer
spam:SPArse Matrix
Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation("spam")' for details.
Maintained by Reinhard Furrer. Last updated 2 months ago.
1 stars 9.36 score 420 scripts 439 dependents