Title: Ratio Benchmarking for Multivariate Small Area Estimation
Maintainer: Zenda Oka Briantiko <221710087@stis.ac.id>
Description: 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).

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

Authors@R: c(person("Nan", "Xiao", email = "me@nanx.me", role = c("aut", "cre")))
Description: Multi-step adaptive elastic-net (MSAENet) algorithm for
feature selection in high-dimensional regressions proposed in

with additional support for multi-step adaptive MCP-Net
(MSAMNet) and multi-step adaptive SCAD-Net (MSASNet) methods.
Imports: glmnet, ncvreg (>= 3.7-0), foreach, mvtnorm, Matrix, lattice

Title: Multi-Step Adaptive Estimation Methods for Sparse Regressions
with additional support for multi-step adaptive MCP-net
(MSAMNet) and multi-step adaptive SCAD-net (MSASNet) methods.
Imports: glmnet, ncvreg (>= 3.8-0), foreach, mvtnorm, survival, Matrix,

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

person("Qing-Song", "Xu", email = "qsxu@csu.edu.cn", role = c("aut")))
with support for multi-step adaptive MCP-net (MSAMNet) and
multi-step adaptive SCAD-net (MSASNet) methods.

Title: Estimate Sample Size to Detect Bacterial Contamination in a
person("Martine", "Ferguson", , "martine.ferguson@fda.hhs.gov", role = c("aut", "cre"),
Description: Estimates the sample size needed to detect microbial contamination
in a lot with a user-specified detection probability and user-specified analytical sensitivity.
Various patterns of microbial contamination are accounted for: homogeneous (Poisson),
heterogeneous (Poisson-Gamma) or localized(Zero-inflated Poisson).
Ida Jongenburger et al. (2010) <doi:10.1016/j.foodcont.2012.02.004>
"Impact of microbial distributions on food safety".
"Casualty Actuarial Society - The Negative Binomial and Poisson Distributions Compared".
Author: Martine Ferguson [aut, cre] (<https://orcid.org/0000-0003-4479-3674>)