Description: An object-oriented modeling language for disciplined convex
programming (DCP). It allows the user to formulate convex optimization problems
in a natural way following mathematical convention and DCP rules. The system
analyzes the problem, verifies its convexity, converts it into a canonical form,
and hands it off to an appropriate solver to obtain the solution.
Imports: methods, R6, Matrix, Rcpp (>= 0.12.12), bit64, gmp, Rmpfr,
Suggests: knitr, rmarkdown, testthat, nnls
License: Apache License 2.0 | file LICENSE
Collate: 'CVXR.R' 'data.R' 'globals.R' 'generics.R' 'utilities.R'
'lin_ops.R' 'atoms.R' 'affine.R' 'problem.R' 'constraints.R'

Suggests: knitr, rmarkdown, testthat, nnls, reticulate, lpSolveAPI,

R.utils, ECOSolveR (>= 0.4), scs (>= 1.2.3), stats

R.utils, ECOSolveR (>= 0.5.1), scs (>= 1.2.3), stats

R.utils, ECOSolveR (>= 0.5.3), scs (>= 1.3), stats

ECOSolveR (>= 0.5.3), scs (>= 1.3), stats, osqp
Suggests: knitr, rmarkdown, testthat, nnls, Rglpk, slam, Rmosek,
Collate: 'CVXR.R' 'data.R' 'globals.R' 'generics.R' 'interface.R'

Description: An object-oriented modeling language for disciplined
convex programming (DCP) as described in Fu, Narasimhan, and Boyd
formulate convex optimization problems in a natural way following
mathematical convention and DCP rules. The system analyzes the
problem, verifies its convexity, converts it into a canonical
form, and hands it off to an appropriate solver to obtain the
solution. Interfaces to solvers on CRAN and elsewhere are
provided, both commercial and open source.
ECOSolveR (>= 0.5.4), scs (>= 3.0), stats, osqp
Suggests: knitr, rmarkdown, testthat, nnls, slam, covr
Enhances: Rcplex, gurobi, rcbc, cccp, Rmosek, Rglpk

ECOSolveR (>= 0.5.4), scs (>= 1.3), stats, osqp

Title: Conditional Variance Estimator for Sufficient Dimension
person("Daniel", "Kapla", role = c("aut", "cph", "cre"), email = "daniel@kapla.at"),
person("Lukas", "Fertl", role = c("aut", "cph")),
person("Efstathia", "Bura", role = "ctb")
Description: Implementation of the CVE (Conditional Variance Estimation) method
proposed by Fertl, L. and Bura, E. (2021) <arXiv:2102.08782> and the ECVE
(Ensemble Conditional Variance Estimation) method introduced in
CVE and ECVE are sufficient dimension reduction methods
in regressions with continuous response and predictors. CVE applies to general
additive error regression models while ECVE generalizes to non-additive error
regression models. They operate under the assumption that the predictors can
be replaced by a lower dimensional projection without loss of information.
It is a semiparametric forward regression model based exhaustive sufficient
dimension reduction estimation method that is shown to be consistent under mild