models.At the heart of it are the vector generalized linear and
additive model (VGLM/VGAM) classes, and the book "Vector Generalized
Linear and Additive Models: With an Implementation in R" (Yee, 2015)
gives details of the statistical framework and VGAM package. Currently
only fixed-effects models are implemented, i.e., no random-effects models.
WWW: https://www.stat.auckland.ac.nz/~yee/VGAM
- Set LICENSE_FILE.
- Depend on devel/yasm instead of building a bundled yasm.
- Remove post-install target, the build system takes care of creating the
proper soversion symlinks.
- Bump PORTREVISION in dependent ports due to a change in the soversion
number.
- update to 1.10
- add YAML option (on by default)
- remove gratuitous word from option descriptions
- USE_PYTHON=concurrent instead of setting EXAMPLESDIR
- alphabetize USE_PYTHON
- discard pkg-message given all options are enabled by default
While here limit python version to 2.x, because it's dependencies
graphics/py-graphviz and math/py-matplotlib failed to configure
with python3.
PR: 204594
Submitted by: John W. O'Brien <john@saltant.com>
Approved by: dikshie@sfc.wide.ad.jp (maintainer)
Estimation and inference methods for models of conditional quantiles:
Linear and nonlinear parametric and non-parametric (total variation
penalized) models for conditional quantiles of a univariate response
and several methods for handling censored survival data. Portfolio
selection methods based on expected shortfall risk are also included.
WWW: https://cran.r-project.org/web/packages/quantreg/
Some basic linear algebra functionality for sparse matrices is
provided: including Cholesky decomposition and backsolving as well
as standard R subsetting and Kronecker products.
WWW: https://cran.r-project.org/web/packages/SparseM/
Test in linear mixed effects models. Attention is on linear mixed
effects models as implemented in the lme4 package. The package
implements a parametric bootstrap test. The package implements a
Kenward-Roger modification of F-tests.
WWW: https://cran.r-project.org/web/packages/pbkrtest/
Fit linear and generalized linear mixed-effects models. The models
and their components are represented using S4 classes and methods.
The core computational algorithms are implemented using the Eigen
C++ library for numerical linear algebra and RcppEigen "glue".
WWW: https://github.com/lme4/lme4/
R and Eigen integration using Rcpp. Eigen is a C++ template library
for linear algebra: matrices, vectors, numerical solvers and related
algorithms. It supports dense and sparse matrices on integer,
floating point and complex numbers, decompositions of such matrices,
and solutions of linear systems. Its performance on many algorithms
is comparable with some of the best implementations based on Lapack
and level-3 BLAS. The RcppEigen package includes the header files
from the Eigen C++ template library (currently version 3.2.2). Thus
users do not need to install Eigen itself in order to use RcppEigen.
Since version 3.1.1, Eigen is licensed under the Mozilla Public
License (version 2); earlier version were licensed under the GNU
LGPL version 3 or later. RcppEigen (the Rcpp bindings/bridge to
Eigen) is licensed under the GNU GPL version 2 or later, as is the
rest of Rcpp.
WWW: https://cran.r-project.org/web/packages/RcppEigen/
nloptr is an R interface to NLopt. NLopt is a free/open-source
library for nonlinear optimization, providing a common interface
for a number of different free optimization routines available
online as well as original implementations of various other algorithms.
WWW: https://cran.r-project.org/web/packages/nloptr/
