Type:	Package
Title:	LIC for Distributed Skewed Regression
Version:	0.3
Date:	2025-07-09
Description:	This comprehensive toolkit for skewed regression is designated as "SLIC" (The LIC for Distributed Skewed Regression Analysis). It is predicated on the assumption that the error term follows a skewed distribution, such as the Skew-Normal, Skew-t, or Skew-Laplace. The methodology and theoretical foundation of the package are described in Guo G.(2020) <doi:10.1080/02664763.2022.2053949>.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Depends:	R (≥ 3.5.0)
Imports:	stats, LaplacesDemon, sn
NeedsCompilation:	no
Author:	Guangbao Guo [aut, cre], Hengxin Gao [aut]
Maintainer:	Guangbao Guo <ggb11111111@163.com>
Suggests:	testthat (≥ 3.0.0)
Config/testthat/edition:	3
Packaged:	2025-07-21 16:07:37 UTC; lenovo
Repository:	CRAN
Date/Publication:	2025-07-25 15:40:11 UTC

Calculate the LIC estimator based on A-optimal and D-optimal criterion

Description

Calculate the LIC estimator based on A-optimal and D-optimal criterion

Usage

LICnew(X, Y, alpha, K, nk)

Arguments

Value

A list containing:

References

Guo, G., Song, H. & Zhu, L. The COR criterion for optimal subset selection in distributed estimation. Statistics and Computing, 34, 163 (2024). doi:10.1007/s11222-024-10471-z

Examples

p = 6; n = 1000; K = 2; nk = 200; alpha = 0.05; sigma = 1
e = rnorm(n, 0, sigma); beta = c(sort(c(runif(p, 0, 1))));
data = c(rnorm(n * p, 5, 10)); X = matrix(data, ncol = p);
Y = X %*% beta + e;
LICnew(X = X, Y = Y, alpha = alpha, K = K, nk = nk)

SLIC function based on LIC with skewed error distributions

Description

The SLIC function extends the LIC method by assuming that the error term follows a skewed distribution (Skew-Normal, Skew-t, or Skew-Laplace), thereby improving the length and information optimisation criterion.

Usage

SLIC(X, Y, alpha = 0.05, K = 10, nk = NULL, dist_type = "skew_normal")

Arguments

Value

MUopt, Bopt, MAEMUopt, MSEMUopt, opt, Yopt

Examples

set.seed(123)
n <- 1000
p <- 5
X <- matrix(rnorm(n * p), ncol = p)
beta <- runif(p, 1, 2)
e <- sn::rsn(n = n, xi = 0, omega = 1, alpha = 5)
Y <- X %*% beta + e
SLIC(X, Y, alpha = 0.05, K = 10, dist_type = "skew_normal")

Caculate the estimators of beta on the A-opt and D-opt

Description

Caculate the estimators of beta on the A-opt and D-opt

Usage

beta_AD(K = K, nk = nk, alpha = alpha, X = X, y = y)

Arguments

Value

A list containing:

References

Guo, G., Song, H. & Zhu, L. The COR criterion for optimal subset selection in distributed estimation. Statistics and Computing, 34, 163 (2024). doi:10.1007/s11222-024-10471-z

Examples

 p=6;n=1000;K=2;nk=200;alpha=0.05;sigma=1
 e=rnorm(n,0,sigma); beta=c(sort(c(runif(p,0,1))));
 data=c(rnorm(n*p,5,10));X=matrix(data, ncol=p);
 y=X%*%beta+e;
 beta_AD(K=K,nk=nk,alpha=alpha,X=X,y=y)

Caculate the estimator of beta on the COR

Description

Caculate the estimator of beta on the COR

Usage

beta_cor(K = K, nk = nk, alpha = alpha, X = X, y = y)

Arguments

Value

A list containing:

References

Guo, G., Song, H. & Zhu, L. The COR criterion for optimal subset selection in distributed estimation. Statistics and Computing, 34, 163 (2024). doi:10.1007/s11222-024-10471-z

Examples

 p=6;n=1000;K=2;nk=200;alpha=0.05;sigma=1
 e=rnorm(n,0,sigma); beta=c(sort(c(runif(p,0,1))));
 data=c(rnorm(n*p,5,10));X=matrix(data, ncol=p);
 y=X%*%beta+e;
 beta_cor(K=K,nk=nk,alpha=alpha,X=X,y=y)

Generate data with skewed errors

Description

Generate data with skewed errors

Usage

serr(n, nr, p, dist_type, ...)

Arguments

Value

A list with X (design matrix), Y (response), and e (error)

Examples

set.seed(123)
data <- serr(1000, 200, 5, "skew_t")
str(data)