person("Charles L.","Lawson", role = "ctb",
person("Richard J.","Hanson", role = "ctb",
Charles L. Lawson [ctb] (file inverse.f),
Richard J. Hanson [ctb] (file inverse.f),
Jack Dongarra [ctb] (files solve.f, inverse.f),

Package: limSolve
Version: 1.5
Date: 2009-04-17
Title: Solving Linear Inverse Models
Author: Karline Soetaert <k.soetaert@nioo.knaw.nl>, Karel Van den
Meersche <k.vdMeersche@nioo.knaw.nl>, Dick van Oevelen
<d.vanoevelen@nioo.knaw.nl>
Maintainer: Karline Soetaert <k.soetaert@nioo.knaw.nl>
Depends: R (>= 2.01), quadprog, lpSolve, MASS
Description: Functions that (1.) Find the minimum/maximum of a linear
or quadratic function: min or max (f(x)), where f(x) =
||Ax-b||^2 or f(x) = sum(ai*xi) subject to equality constraints
Ex=f and/or inequality constraints Gx>=h. (2.) Sample an
underdetermined- or overdetermined system Ex=f subject to
Gx>=h, and if applicable Ax~=b. (3.) Solve a linear system Ax=B
for the unknown x. Includes banded and tridiagonal linear
systems. The package calls Fortran functions from LINPACK
License: GPL
LazyData: yes
Packaged: Sun Apr 19 00:46:54 2009; zeileis
Repository: CRAN
Date/Publication: 2009-04-20 20:33:54

Title: Local Interpretable Model-Agnostic Explanations
Authors@R: c(person('Thomas Lin', 'Pedersen', role = c('cre', 'aut'), email = "thomasp85@gmail.com"),
person('Michaël', 'Benesty', role = c('aut'), email = "michael@benesty.fr"))
Description: When building complex models, it is often difficult to explain why
the model should be trusted. While global measures such as accuracy are
useful, they cannot be used for explaining why a model made a specific
prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for
explaining the outcome of black box models by fitting a local model around
the point in question an perturbations of this point. The approach is
described in more detail in the article by Ribeiro et al. (2016)
Imports: glmnet, stats, ggplot2, tools, stringi, Matrix, stringdist,
Rcpp, assertthat, htmlwidgets, shiny, shinythemes
Suggests: xgboost, testthat, mlr, text2vec, MASS, covr, knitr,