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r-forge
Matrix:Sparse and Dense Matrix Classes and Methods
A rich hierarchy of sparse and dense matrix classes, including general, symmetric, triangular, and diagonal matrices with numeric, logical, or pattern entries. Efficient methods for operating on such matrices, often wrapping the 'BLAS', 'LAPACK', and 'SuiteSparse' libraries.
Maintained by Martin Maechler. Last updated 21 days ago.
1 stars 17.23 score 33k scripts 12k dependents
hwborchers
pracma:Practical Numerical Math Functions
Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.
Maintained by Hans W. Borchers. Last updated 1 years ago.
29 stars 12.34 score 6.6k scripts 931 dependents
shabbychef
madness:Automatic Differentiation of Multivariate Operations
An object that supports automatic differentiation of matrix- and multidimensional-valued functions with respect to multidimensional independent variables. Automatic differentiation is via 'forward accumulation'.
Maintained by Steven E. Pav. Last updated 4 years ago.
31 stars 6.59 score 28 scripts 3 dependents
bgreenwell
ramify:Additional Matrix Functionality
Additional matrix functionality for R including: (1) wrappers for the base matrix function that allow matrices to be created from character strings and lists (the former is especially useful for creating block matrices), (2) better printing of large matrices via the generic "pretty" print function, and (3) a number of convenience functions for users more familiar with other scientific languages like 'Julia', 'Matlab'/'Octave', or 'Python'+'NumPy'.
Maintained by Brandon Greenwell. Last updated 8 years ago.
3 stars 6.32 score 154 scripts 3 dependents
francescobartolucci
extRC:Extended RC Models for Contingency Tables
Maximum likelihood estimation of an extended class of row-column (RC) association models for two-dimensional contingency tables, which are formulated by a condition of reduced rank on a matrix of extended association parameters; see Forcina (2019) <arXiv:1910.13848>. These parameters are defined by choosing the logit type for the row and column variables among four different options and a transformation derived from suitable divergence measures.
Maintained by Francesco Bartolucci. Last updated 4 years ago.
1.00 score 10 scripts