Ratio Benchmarking for Multivariate Small Area Estimation
Zenda Oka Briantiko [aut, cre],
Azka Ubaidillah [aut]
Implements multivariate ratio benchmarking small area estimation. This package provides ratio benchmarking estimation for univariate and multivariate small area estimation and its MSE. In fact, MSE estimators for ratio benchmark are not readily available, so resampling method that called parametric bootstrap is applied. The ratio benchmark model and parametric bootstrap in this package are based on the model proposed in small area estimation. J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7).

Multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions.
glmnet, foreach, mvtnorm, Matrix, lattice

c(person("Nan", "Xiao", email = "me@nanx.me", role = c("aut", "cre")))
Multi-step adaptive elastic-net (MSAENet) algorithm for
feature selection in high-dimensional regressions proposed in
Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>.

Multi-step adaptive elastic-net (MSAENet) algorithm for
feature selection in high-dimensional regressions proposed in
Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>,
with additional support for multi-step adaptive MCP-Net
(MSAMNet) and multi-step adaptive SCAD-Net (MSASNet) methods.
glmnet, ncvreg (>= 3.7-0), foreach, mvtnorm, Matrix, lattice

Multi-Step Adaptive Estimation Methods for Sparse Regressions
Multi-step adaptive elastic-net (MSAENet) algorithm for
feature selection in high-dimensional regressions proposed in
Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>,
with additional support for multi-step adaptive MCP-net
(MSAMNet) and multi-step adaptive SCAD-net (MSASNet) methods.
glmnet, ncvreg (>= 3.8-0), foreach, mvtnorm, survival, Matrix,
lattice

glmnet, ncvreg (>= 3.8-0), foreach, mvtnorm, survival, Matrix

c(
person("Nan", "Xiao", email = "me@nanx.me", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-0250-5673")),
person("Qing-Song", "Xu", email = "qsxu@csu.edu.cn", role = c("aut")))
Multi-step adaptive elastic-net (MSAENet) algorithm for
feature selection in high-dimensional regressions proposed in
Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>,
with support for multi-step adaptive MCP-net (MSAMNet) and
multi-step adaptive SCAD-net (MSASNet) methods.