c(
person("Anqi", "Fu",
role=c("aut", "cre"),
email = "anqif@alumni.stanford.edu"),
person("Balasubramanian", "Narasimhan",
role=c("aut"),
email = "naras@stat.stanford.edu"),
person("David W", "Kang",
role=c("aut"),
email = "Dkang9503@gmail.com"),
person("Steven", "Diamond",
role=c("aut"),
email = "stevend2@stanford.edu"),
person("John", "Miller",
role=c("aut"),
email = "miller_john@berkeley.edu"),
person("Stephen", "Boyd",
role=c("ctb"),
email = "boyd@stanford.edu"),
person("Paul Kunsberg", "Rosenfield",
role=c("ctb"),
email = "prosenfield@farmersbusinessnetwork.com")
)
An object-oriented modeling language for disciplined
convex programming (DCP) as described in Fu, Narasimhan, and Boyd
(2020, <doi:10.18637/jss.v094.i14>). 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. Interfaces to solvers on CRAN and elsewhere are
provided, both commercial and open source.
methods, R6, Matrix, Rcpp (>= 0.12.12), bit64, gmp, Rmpfr,
ECOSolveR (>= 0.5.4), scs (>= 3.0), stats, osqp
knitr, rmarkdown, testthat, nnls, slam, covr
Rcplex, gurobi, rcbc, cccp, Rmosek, Rglpk

methods, R6, Matrix, Rcpp (>= 0.12.12), bit64, gmp, Rmpfr,
ECOSolveR (>= 0.5.4), scs (>= 1.3), stats, osqp

knitr, rmarkdown, testthat, nnls, Rglpk, slam, Rmosek,
gurobi, Rcplex, rcbc

Conditional Variance Estimator for Sufficient Dimension
Reduction
Daniel Kapla [aut, cph, cre],
Lukas Fertl [aut, cph],
Efstathia Bura [ctb]
c(
person("Daniel", "Kapla", role = c("aut", "cph", "cre"), email = "daniel@kapla.at"),
person("Lukas", "Fertl", role = c("aut", "cph")),
person("Efstathia", "Bura", role = "ctb")
)
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
Fertl, L. and Bura, E. (2021) <arXiv:2102.13435>.
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
assumptions.