blocks|key|168713|text|方法1:|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|168714|报告的错误是无法打开连接。在Windows中，这就是通常是防火墙问题，并出现在Windows中。通常的第一次尝试应该是运行internet2.dll。在控制台会话中，可以使用：|168715|setInternet2(TRUE)|code-block|syntax|javascript|168716|R版3.3.1补丁新闻(2016-09-13+r71247)+(仅限Windows)函数setInternet2()没有任何效果，并将在适当的时候删除。方法“内部”和"wininet“之间的选择现在由url()和download.file()的方法参数进行选择，它们的默认值可以通过选项来设置。开箱即用的默认值仍然是"wininet“(自R3.2.2以来一直是如此)|168717|您使用的是3.3.1版本，这就是它不再工作的原因。|168718|方法2|168719|错误是该包需要另一个不可用的包bitops。该包不存在于任何依赖项中，但可能其中一个依赖项依次要求它(在本例中是:+ROCR)。|CODE|168720|试着安装：|168721|install.packages("bitops",repos="https://cran.r-project.org/bin/windows/contrib/3.3/bitops_1.0-6.zip",dependencies=TRUE,type="source")+|168722|包DMwR包含包abind，动物园，xts，quantmod和ROCR作为进口。因此，除了安装5个软件包，您必须安装DMwR软件包，手动安装这些软件包。|168723|按以下顺序安装软件包：|168724|+install.packages('abind')
+install.packages('zoo')
+install.packages('xts')
+install.packages('quantmod')
+install.packages('ROCR')
+install.packages("DMwR")
+library("DMwR")|168725|方法3|168726|chooseCRANmirror()+|168727|​|168728|📷|atomic|168729|从弹出列表中选择CRAN镜像。然后安装软件包：|168730|install.packages("bitops")
install.packages("DMwR")|168731|entityMap|0|LINK|mutability|MUTABLE|url|http://cran.r-project.org/bin/windows/base/rw-FAQ.html#The-Internet-download-functions-fail_002e|1|http://r.meteo.uni.wroc.pl/doc/manuals/r-patched/NEWS.pdf|2|http://www.crifan.com/r_language_run_code_error_could_not_find_function_geturl/|3|IMAGE|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/361f38e8ad63050512470d114baefc70.png|imageAlt^0|0|4|0|Q|L|0|0|0|0|U|1|0|0|0|3|0|F|6|0|0|0|0|0|0|0|3|2|0|0|0|0|1|3|0|0|0^^$0|@$1|2|3|4|5|6|7|20|8|@$9|21|A|22|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|23|8|@]|D|@$9|24|A|25|1|26]]|E|$]]|$1|H|3|I|5|J|7|27|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|28|8|@]|D|@$9|29|A|2A|1|2B]]|E|$]]|$1|O|3|P|5|6|7|2C|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|2D|8|@$9|2E|A|2F|B|C]]|D|@]|E|$]]|$1|S|3|T|5|6|7|2G|8|@$9|2H|A|2I|B|U]]|D|@]|E|$]]|$1|V|3|W|5|6|7|2J|8|@]|D|@]|E|$]]|$1|X|3|Y|5|J|7|2K|8|@]|D|@]|E|$K|L]]|$1|Z|3|10|5|6|7|2L|8|@]|D|@]|E|$]]|$1|11|3|12|5|6|7|2M|8|@]|D|@]|E|$]]|$1|13|3|14|5|J|7|2N|8|@]|D|@]|E|$K|L]]|$1|15|3|16|5|6|7|2O|8|@]|D|@$9|2P|A|2Q|1|2R]]|E|$]]|$1|17|3|18|5|J|7|2S|8|@]|D|@]|E|$K|L]]|$1|19|3|1A|5|6|7|2T|8|@]|D|@]|E|$]]|$1|1B|3|1C|5|1D|7|2U|8|@]|D|@$9|2V|A|2W|1|2X]]|E|$]]|$1|1E|3|1F|5|6|7|2Y|8|@]|D|@]|E|$]]|$1|1G|3|1H|5|J|7|2Z|8|@]|D|@]|E|$K|L]]|$1|1I|3|-4|5|6|7|30|8|@]|D|@]|E|$]]]|1J|$1K|$5|1L|1M|1N|E|$1O|1P]]|1Q|$5|1L|1M|1N|E|$1O|1R]]|1S|$5|1L|1M|1N|E|$1O|1T]]|1U|$5|1V|1M|1W|E|$1X|1Y|1Z|-4]]]]

Approach 1:

The error being reported is inability to open a connection. In Windows that is <a href="http://cran.r-project.org/bin/windows/base/rw-FAQ.html#The-Internet-download-functions-fail_002e" rel="nofollow noreferrer">often a firewall problem and is in the Windows R FAQ</a>. The usual first attempt should be to run internet2.dll. From a console session you can use:

<pre><code>setInternet2(TRUE)
</code></pre>

<a href="http://r.meteo.uni.wroc.pl/doc/manuals/r-patched/NEWS.pdf" rel="nofollow noreferrer">NEWS for R version 3.3.1 Patched (2016-09-13 r71247)</a> 
(Windows only) Function
setInternet2()
has no effect and will be removed in due
course. The choice between methods
"internal"
and
"wininet"
is now made by the
method
arguments of
url()
and
download.file()
and their defaults can be set
via
options. The out-of-the-box default remains
"wininet"
(as it has been since
R
3.2.2) 

You are using version 3.3.1, this is why it is not working anymore. 

Approach 2

The error is suggesting that the package requires another package <code>bitops</code> that is not available. That package is not in any of the dependencies but perhaps one of the dependencies requires it in turn(In this case, it is: ROCR). 

Try installing:

<pre><code>install.packages("bitops",repos="https://cran.r-project.org/bin/windows/contrib/3.3/bitops_1.0-6.zip",dependencies=TRUE,type="source") 
</code></pre>

The package DMwR contains packages abind, zoo, xts, quantmod and ROCR as imports. So, additionally to installing 5 packages you must install DMwR package, Install these packages manually.

Install packages in following sequence: 

<pre><code> install.packages('abind')
 install.packages('zoo')
 install.packages('xts')
 install.packages('quantmod')
 install.packages('ROCR')
 install.packages("DMwR")
 library("DMwR")
</code></pre>

<a href="http://www.crifan.com/r_language_run_code_error_could_not_find_function_geturl/" rel="nofollow noreferrer">Approach 3</a>:

<pre><code>chooseCRANmirror() 
</code></pre>

<a href="https://i.stack.imgur.com/izUIo.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/izUIo.png" alt="enter image description here"></a>
 Select CRAN mirror from popup list. Then install packages:

<pre><code>install.packages("bitops")
install.packages("DMwR")
</code></pre>

blocks|key|168739|text|原因是软件包'DMwR‘是在R版本3.4.3下构建的。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|168740|因此，这个解决方案实际上是在标记的答案中详细解释的。|168741|因此，简短：|168742|只需运行下面的脚本，以解决问题！|168743|install.packages('abind')
install.packages('zoo')
install.packages('xts')
install.packages('quantmod')
install.packages('ROCR')
install.packages("DMwR")

library("DMwR")|code-block|syntax|javascript|168744|entityMap^0|7|4|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|V|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|W|8|@]|D|@]|E|$]]|$1|J|3|K|5|6|7|X|8|@]|D|@]|E|$]]|$1|L|3|M|5|N|7|Y|8|@]|D|@]|E|$O|P]]|$1|Q|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|R|$]]

The reason is that the package 'DMwR' was built under R version 3.4.3 So the solution is actually explained in the marked answer in details. Hence, to be short: Just run the script below to get the problem solved!  

<pre><code>install.packages('abind')
install.packages('zoo')
install.packages('xts')
install.packages('quantmod')
install.packages('ROCR')
install.packages("DMwR")

library("DMwR")
</code></pre>

blocks|key|168782|text|包‘DMwR’被从CRAN存储库中删除。以前可用的版本可以从归档获得。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|168783|https://CRAN.R-project.org/package=DMwR|168784|您可以使用在CRAN包中编写的函数。将以下代码复制到一个新的RScript中，运行它并保存它，以便将来使用。一旦您运行了这个函数，您应该能够使用您尝试使用它的方式。|168785|#+===================================================
#+Creating+a+SMOTE+training+sample+for+classification+problems
#
#+If+called+with+learner=NULL+(the+default)+is+does+not
#+learn+any+model,+simply+returning+the+SMOTEd+data+set
#
#+NOTE:+It+does+not+handle+NAs!
#
#+Examples:
#++ms+<-+SMOTE(Species+~+.,iris,'setosa',perc.under=400,perc.over=300,
#++++++++++++++++learner='svm',gamma=0.001,cost=100)
#++newds+<-+SMOTE(Species+~+.,iris,'setosa',perc.under=300,k=3,perc.over=400)
#
#+L.+Torgo,+Feb+2010
#+---------------------------------------------------
SMOTE+<-+function(form,data,
++++++++++++++++++perc.over=200,k=5,
++++++++++++++++++perc.under=200,
++++++++++++++++++learner=NULL,...
++++++++++++++++++)
++
++#+INPUTS:
++#+form+a+model+formula
++#+data+the+original+training+set+(with+the+unbalanced+distribution)
++#+minCl++the+minority+class+label
++#+per.over/100+is+the+number+of+new+cases+(smoted+cases)+generated
++#++++++++++++++for+each+rare+case.+If+perc.over+<+100+a+single+case
++#++++++++++++++is+generated+uniquely+for+a+randomly+selected+perc.over
++#++++++++++++++of+the+rare+cases
++#+k+is+the+number+of+neighbours+to+consider+as+the+pool+from+where
++#+++the+new+examples+are+generated
++#+perc.under/100+is+the+number+of+"normal"+cases+that+are+randomly
++#++++++++++++++++selected+for+each+smoted+case
++#+learner+the+learning+system+to+use.
++#+...++any+learning+parameters+to+pass+to+learner
{

++#+the+column+where+the+target+variable+is
++tgt+<-+which(names(data)+==+as.character(form[[2]]))
++minCl+<-+levels(data[,tgt])[which.min(table(data[,tgt]))]
++
++#+get+the+cases+of+the+minority+class
++minExs+<-+which(data[,tgt]+==+minCl)

++#+generate+synthetic+cases+from+these+minExs
++if+(tgt+<+ncol(data))+{
++++++cols+<-+1:ncol(data)
++++++cols[c(tgt,ncol(data))]+<-+cols[c(ncol(data),tgt)]
++++++data+<-++data[,cols]
++}
++newExs+<-+smote.exs(data[minExs,],ncol(data),perc.over,k)
++if+(tgt+<+ncol(data))+{
++++++newExs+<-+newExs[,cols]
++++++data+<-+data[,cols]
++}
++
++#+get+the+undersample+of+the+"majority+class"+examples
++selMaj+<-+sample((1:NROW(data))[-minExs],
+++++++++++++++++++as.integer((perc.under/100)*nrow(newExs)),
+++++++++++++++++++replace=T)

++#+the+final+data+set+(the+undersample%2Bthe+rare+cases%2Bthe+smoted+exs)
++newdataset+<-+rbind(data[selMaj,],data[minExs,],newExs)

++#+learn+a+model+if+required
++if+(is.null(learner))+return(newdataset)
++else+do.call(learner,list(form,newdataset,...))
}



#+===================================================
#+Obtain+a+set+of+smoted+examples+for+a+set+of+rare+cases.
#+L.+Torgo,+Feb+2010
#+---------------------------------------------------
smote.exs+<-+function(data,tgt,N,k)
++#+INPUTS:
++#+data+are+the+rare+cases+(the+minority+"class"+cases)
++#+tgt+is+the+name+of+the+target+variable
++#+N+is+the+percentage+of+over-sampling+to+carry+out;
++#+and+k+is+the+number+of+nearest+neighours+to+use+for+the+generation
++#+OUTPUTS:
++#+The+result+of+the+function+is+a+(N/100)*T+set+of+generated
++#+examples+with+rare+values+on+the+target
{
++nomatr+<-+c()
++T+<-+matrix(nrow=dim(data)[1],ncol=dim(data)[2]-1)
++for(col+in+seq.int(dim(T)[2]))
++++if+(class(data[,col])+%25in%25+c('factor','character'))+{
++++++T[,col]+<-+as.integer(data[,col])
++++++nomatr+<-+c(nomatr,col)
++++}+else+T[,col]+<-+data[,col]
++
++if+(N+<+100)+{+#+only+a+percentage+of+the+T+cases+will+be+SMOTEd
++++nT+<-+NROW(T)
++++idx+<-+sample(1:nT,as.integer((N/100)*nT))
++++T+<-+T[idx,]
++++N+<-+100
++}

++p+<-+dim(T)[2]
++nT+<-+dim(T)[1]

++ranges+<-+apply(T,2,max)-apply(T,2,min)
++
++nexs+<-++as.integer(N/100)+#+this+is+the+number+of+artificial+exs+generated
++++++++++++++++++++++++++++++++++++++++#+for+each+member+of+T
++new+<-+matrix(nrow=nexs*nT,ncol=p)++++#+the+new+cases

++for(i+in+1:nT)+{

++++#+the+k+NNs+of+case+T[i,]
++++xd+<-+scale(T,T[i,],ranges)
++++for(a+in+nomatr)+xd[,a]+<-+xd[,a]==0
++++dd+<-+drop(xd%5E2+%25*%25+rep(1,+ncol(xd)))
++++kNNs+<-+order(dd)[2:(k%2B1)]

++++for(n+in+1:nexs)+{
++++++#+select+randomly+one+of+the+k+NNs
++++++neig+<-+sample(1:k,1)

++++++ex+<-+vector(length=ncol(T))

++++++#+the+attribute+values+of+the+generated+case
++++++difs+<-+T[kNNs[neig],]-T[i,]
++++++new[(i-1)*nexs%2Bn,]+<-+T[i,]%2Brunif(1)*difs
++++++for(a+in+nomatr)
++++++++new[(i-1)*nexs%2Bn,a]+<-+c(T[kNNs[neig],a],T[i,a])[1%2Bround(runif(1),0)]

++++}
++}
++newCases+<-+data.frame(new)
++for(a+in+nomatr)
++++newCases[,a]+<-+factor(newCases[,a],levels=1:nlevels(data[,a]),labels=levels(data[,a]))

++newCases[,tgt]+<-+factor(rep(data[1,tgt],nrow(newCases)),levels=levels(data[,tgt]))
++colnames(newCases)+<-+colnames(data)
++newCases
}|code-block|syntax|javascript|168786|entityMap|0|LINK|mutability|MUTABLE|url|https://cran.r-project.org/src/contrib/Archive/DMwR/|1|https://cran.r-project.org/package=DMwR^0|U|2|0|0|0|13|1|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@$A|X|B|Y|1|Z]]|C|$]]|$1|D|3|E|5|6|7|10|8|@]|9|@$A|11|B|12|1|13]]|C|$]]|$1|F|3|G|5|6|7|14|8|@]|9|@]|C|$]]|$1|H|3|I|5|J|7|15|8|@]|9|@]|C|$K|L]]|$1|M|3|-4|5|6|7|16|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]|U|$5|P|Q|R|C|$S|V]]]]

Package ‘DMwR’ was removed from the CRAN repository.
Formerly available versions can be obtained from the <a href="https://cran.r-project.org/src/contrib/Archive/DMwR/" rel="nofollow noreferrer">archive</a>.
<a href="https://CRAN.R-project.org/package=DMwR" rel="nofollow noreferrer">https://CRAN.R-project.org/package=DMwR</a>
You can use the function as written in CRAN package. Copy the following code in a new RScript, run it and save it for future use if you want. Once you run this function, you should be able to use the way you have been trying to use it.
<pre><code># ===================================================
# Creating a SMOTE training sample for classification problems
#
# If called with learner=NULL (the default) is does not
# learn any model, simply returning the SMOTEd data set
#
# NOTE: It does not handle NAs!
#
# Examples:
# ms &lt;- SMOTE(Species ~ .,iris,'setosa',perc.under=400,perc.over=300,
# learner='svm',gamma=0.001,cost=100)
# newds &lt;- SMOTE(Species ~ .,iris,'setosa',perc.under=300,k=3,perc.over=400)
#
# L. Torgo, Feb 2010
# ---------------------------------------------------
SMOTE &lt;- function(form,data,
 perc.over=200,k=5,
 perc.under=200,
 learner=NULL,...
 )
 
 # INPUTS:
 # form a model formula
 # data the original training set (with the unbalanced distribution)
 # minCl the minority class label
 # per.over/100 is the number of new cases (smoted cases) generated
 # for each rare case. If perc.over &lt; 100 a single case
 # is generated uniquely for a randomly selected perc.over
 # of the rare cases
 # k is the number of neighbours to consider as the pool from where
 # the new examples are generated
 # perc.under/100 is the number of &quot;normal&quot; cases that are randomly
 # selected for each smoted case
 # learner the learning system to use.
 # ... any learning parameters to pass to learner
{

 # the column where the target variable is
 tgt &lt;- which(names(data) == as.character(form[[2]]))
 minCl &lt;- levels(data[,tgt])[which.min(table(data[,tgt]))]
 
 # get the cases of the minority class
 minExs &lt;- which(data[,tgt] == minCl)

 # generate synthetic cases from these minExs
 if (tgt &lt; ncol(data)) {
 cols &lt;- 1:ncol(data)
 cols[c(tgt,ncol(data))] &lt;- cols[c(ncol(data),tgt)]
 data &lt;- data[,cols]
 }
 newExs &lt;- smote.exs(data[minExs,],ncol(data),perc.over,k)
 if (tgt &lt; ncol(data)) {
 newExs &lt;- newExs[,cols]
 data &lt;- data[,cols]
 }
 
 # get the undersample of the &quot;majority class&quot; examples
 selMaj &lt;- sample((1:NROW(data))[-minExs],
 as.integer((perc.under/100)*nrow(newExs)),
 replace=T)

 # the final data set (the undersample+the rare cases+the smoted exs)
 newdataset &lt;- rbind(data[selMaj,],data[minExs,],newExs)

 # learn a model if required
 if (is.null(learner)) return(newdataset)
 else do.call(learner,list(form,newdataset,...))
}



# ===================================================
# Obtain a set of smoted examples for a set of rare cases.
# L. Torgo, Feb 2010
# ---------------------------------------------------
smote.exs &lt;- function(data,tgt,N,k)
 # INPUTS:
 # data are the rare cases (the minority &quot;class&quot; cases)
 # tgt is the name of the target variable
 # N is the percentage of over-sampling to carry out;
 # and k is the number of nearest neighours to use for the generation
 # OUTPUTS:
 # The result of the function is a (N/100)*T set of generated
 # examples with rare values on the target
{
 nomatr &lt;- c()
 T &lt;- matrix(nrow=dim(data)[1],ncol=dim(data)[2]-1)
 for(col in seq.int(dim(T)[2]))
 if (class(data[,col]) %in% c('factor','character')) {
 T[,col] &lt;- as.integer(data[,col])
 nomatr &lt;- c(nomatr,col)
 } else T[,col] &lt;- data[,col]
 
 if (N &lt; 100) { # only a percentage of the T cases will be SMOTEd
 nT &lt;- NROW(T)
 idx &lt;- sample(1:nT,as.integer((N/100)*nT))
 T &lt;- T[idx,]
 N &lt;- 100
 }

 p &lt;- dim(T)[2]
 nT &lt;- dim(T)[1]

 ranges &lt;- apply(T,2,max)-apply(T,2,min)
 
 nexs &lt;- as.integer(N/100) # this is the number of artificial exs generated
 # for each member of T
 new &lt;- matrix(nrow=nexs*nT,ncol=p) # the new cases

 for(i in 1:nT) {

 # the k NNs of case T[i,]
 xd &lt;- scale(T,T[i,],ranges)
 for(a in nomatr) xd[,a] &lt;- xd[,a]==0
 dd &lt;- drop(xd^2 %*% rep(1, ncol(xd)))
 kNNs &lt;- order(dd)[2:(k+1)]

 for(n in 1:nexs) {
 # select randomly one of the k NNs
 neig &lt;- sample(1:k,1)

 ex &lt;- vector(length=ncol(T))

 # the attribute values of the generated case
 difs &lt;- T[kNNs[neig],]-T[i,]
 new[(i-1)*nexs+n,] &lt;- T[i,]+runif(1)*difs
 for(a in nomatr)
 new[(i-1)*nexs+n,a] &lt;- c(T[kNNs[neig],a],T[i,a])[1+round(runif(1),0)]

 }
 }
 newCases &lt;- data.frame(new)
 for(a in nomatr)
 newCases[,a] &lt;- factor(newCases[,a],levels=1:nlevels(data[,a]),labels=levels(data[,a]))

 newCases[,tgt] &lt;- factor(rep(data[1,tgt],nrow(newCases)),levels=levels(data[,tgt]))
 colnames(newCases) &lt;- colnames(data)
 newCases
}
</code></pre>

blocks|key|168810|text|它已经被从CRAN图书馆移走了。有关于如何从存档中检索它的说明。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|168811|要么遵循链接-+https://packagemanager.rstudio.com/client/#/repos/2/packages/DMwR|offset|length|style|BOLD|168812|或复制粘贴下面提到的三行代码：|168813|install.packages("devtools")
devtools::install_version('DMwR',+'0.4.1')
library("DMwR")|code-block|syntax|javascript|168814|编辑:这是我在2022年下载DMwR软件包时遇到的错误，但是当问题发布时，错误发生的另一个原因。|168815|entityMap|0|LINK|mutability|MUTABLE|url|https://packagemanager.rstudio.com/client/#/repos/2/packages/DMwR^0|0|0|7|8|1T|0|0|0|F|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Z|8|@$D|10|E|11|F|G]]|9|@$D|12|E|13|1|14]]|A|$]]|$1|H|3|I|5|6|7|15|8|@$D|16|E|17|F|G]]|9|@]|A|$]]|$1|J|3|K|5|L|7|18|8|@]|9|@]|A|$M|N]]|$1|O|3|P|5|6|7|19|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|1A|8|@]|9|@]|A|$]]]|R|$S|$5|T|U|V|A|$W|X]]]]

It has been removed from the CRAN library. There are instructions on how to retrieve it from the archive.
Either follow the link - <a href="https://packagemanager.rstudio.com/client/#/repos/2/packages/DMwR" rel="nofollow noreferrer">https://packagemanager.rstudio.com/client/#/repos/2/packages/DMwR</a>
OR copy-paste the three lines of code mentioned below:
<pre><code>install.packages(&quot;devtools&quot;)
devtools::install_version('DMwR', '0.4.1')
library(&quot;DMwR&quot;)
</code></pre>
EDIT: this is the error I got while downloading the DMwR package in 2022, but looks like when the question was posted, the error happened because of another reason.

hi i am getting this error message while installing DMwR package from RGUI-3.3.1.

<pre><code>Error in read.dcf(file.path(pkgname, "DESCRIPTION"), c("Package", "Type")) : 
cannot open the connection
In addition: Warning messages:
1: In unzip(zipname, exdir = dest) : error 1 in extracting from zip file
2: In read.dcf(file.path(pkgname, "DESCRIPTION"), c("Package", "Type")) :
cannot open compressed file 'bitops/DESCRIPTION', probable reason 'No such file or directory'
</code></pre>

getting Error while installing DMwR package

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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社区新版编辑器体验调研

嗨，我在从RGUI-3.3.1安装DMwR软件包时收到了这个错误消息。Error in read.dcf(file.path(pkgname, "DESCRIPTION"), c("Package", "Type")) : cannot open the connectionIn addition: Warning m...

问安装DMwR包时出错
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