如何防止“以下对象被隐藏在‘package：.’：.”当装载一个包裹时，完全的故障/警告？

Question

当我加载我创建的包时，我会收到以下警告：

“下面的对象是从‘package:utils’：combn中屏蔽的”

即使我在R的工作目录中的"R“文件夹的.R文件中使用了组合：：梳函数定义。

难道没有办法摆脱"...object被蒙蔽了.“警告？(我根本不想掩饰梳子，请看下面Gregor的帖子。)

我在包中的函数定义中添加了“组合：：”前缀，只要我想使用组合词的组合键，就不会得到上述警告(组合词：：组合词与utils：：combn的混合)。

gctemplate 是包因果查找器的一个函数(对于想要掌握该功能的人，我删除了“组合：：”；当我在函数gctemplate中看到组合框时，仍然收到上述警告)：

#' gctemplate
#'
#' Granger causality template for a given system in the desired pattern.
#'
#' Assume a system in which its Granger causality relations among its variables will be searched is given. For the variables of this system (with nvars variables), some varibles may cause (causers, like independents) some others (those that are caused by causers, like dependents) conditional on some others in the system. The function gctemplate has three arguements to list all the possible causation pattern in which Granger causality will be searched. As is known, dependent and independent variables are not specified in the beginning of a Granger analysis, the analysis reveals them.
#'
#' @param nvars integer. The number of all the variables in a given system.
#' @param ncausers integer. The selected number of causer (independent-like) variables
#' @param ndependents integer. The selected number of variables that are caused by the causers, number of dependent-like variables
#' @return granger causality template
#' @author Erdogan Cevher erdogancevher@@gmail.com
#' @seealso \code{\link{conditionalGb}}
#' @examples
#' ## List all G-causalities in a VAR system of 5 variables that will be searched in the pattern of 1 
#' ## causer (like-independent) variable and 2 like-dependents conditional on 5-(1+2)=2 of the remaining 
#' ## variable(s) in the system. Varibles are assigned to numbers 1 to nvars. 
#' ## "1 2 5 3 4" in the rsulting line of gctemplate is to indicate the 
#' ## (conditonal, partial, etc.) G-causality from variable 1 to varibles 2 and 5 
#' ## conditonal on variables 3 and 4.
#' # gctemplate(5,1,2)
#' ## The number of all G-causalities to be searched in the above pattern.
#' #dim(gctemplate(5,1,2))[[1]]
#' @importFrom combinat combn
#' @export
gctemplate <- function(nvars, ncausers, ndependents){  
independents <- combn(nvars, ncausers)  
patinajnumber <-  dim(combn(nvars - ncausers, ndependents))[[2]]  
independentspatinajednumber <- dim(combn(nvars, ncausers))[[2]]*patinajnumber   
dependents <- matrix(, nrow = dim(combn(nvars, ncausers))[[2]]*patinajnumber, ncol = ndependents)  
for (i in as.integer(1:dim(combn(nvars, ncausers))[[2]])){     
dependents[(patinajnumber*(i-1)+1):(patinajnumber*i),] <- t(combn(setdiff(seq(1:nvars), independents[,i]), ndependents))  
}  
independentspatinajed <- matrix(, nrow = dim(combn(nvars, ncausers))[[2]]*patinajnumber, ncol = ncausers)  
for (i in as.integer(1:dim(combn(nvars, ncausers))[[2]])){     
for (j in as.integer(1:patinajnumber)){     
independentspatinajed[(i-1)*patinajnumber+j,] <- independents[,i]    
}}  
independentsdependents <- cbind(independentspatinajed, dependents)  
others <- matrix(, nrow = dim(combn(nvars, ncausers))[[2]]*patinajnumber, ncol = nvars - ncausers - ndependents)   
for (i in as.integer(1:((dim(combn(nvars, ncausers))[[2]])*patinajnumber))){    
others[i, ]  <- setdiff(seq(1:nvars), independentsdependents[i,])   
}  
causalitiestemplate <- cbind(independentsdependents, others)  
causalitiestemplate  
}  

以下是DESCRIPTION文件的内容：

Package: causfinder  
Type: Package  
Title: A visual analyser for pairwise and multivariate conditional and partial  
Granger causalities. For a list of documented functions, use library(help =  
"causfinder")  
Version: 2.0  
Date: 2014-10-05  
Author: Erdogan CEVHER <erdogancevher@gmail.com>  
Maintainer: Erdogan CEVHER <erdogancevher@gmail.com>  
Description: Given a system of finite number of variables with their data in a  
matrix, the functions in the causfinder package analyze the pairwise and  
multivariate conditional and partial Granger causalities in a systematic  
and overall approach. By "overall approach" we mean that all the  
G-causalities among the variables in a certain desired pattern are  
calculated (the user then can retrieve his/her G-causality in interest as  
one element of the pattern). This pattern may correspond to pairwise or  
multivariate Granger causalities. Some of the functions in the package use  
bootstrapping whereas some others not. The analysis of the Granger  
causalities are performed via various ways; i.e., presenting the F  
statistics, p values, lower bounds and upper bounds of the bootstrapping  
values etc., and via presenting various plots and grid matrixes. For a list  
of documented functions, use library(help = "causfinder")  
Depends:  
    R (>= 3.0.2)  
Imports:  
    np(>= 0.50-1),RColorBrewer(>= 1.0-5),grid,scales(>= 0.2.4),boot(>= 1.3-11),calibrate(>= 1.7.2),combinat(>= 0.0-8),corpcor(>= 1.6.6)  
License: GPL (>= 3)  
LazyLoad: yes  
LazyData: yes  
URL: https://github.com/erdogancevher/causfinder  

注意：将组合包的函数梳重命名为combnnn，并将其内容( .R文件等)放入其中。到我的包R文件夹；并且应用重定位过程可能不会导致上述警告。我试图获得的只是一种系统性的方法来防止警告，而不是我在本说明中描述的非常棘手/漫长的方式。

Answer 1

如果路径的上级部分中的对象(对象遮蔽了您真正想要的对象)是您可以不用的对象，则可以使用以下命令

rm(object_name)

在做时：

需求(ISLR)

我收到这样的信息：

The following object is masked _by_ ‘.GlobalEnv’

所以它是通过做

rm(Auto)

我现在参考的是ISLR软件包中的Auto，而不是GlobalEnv