similar to: regarding installation of bayesQR package

I typed the following command *install.packages('bayesQR')'* to install bayesQR(my R version is 2.14) i am encountered the following error. Installing package(s) into ?C:/Users/knreddy.IDRBTVM/Documents/R/win-library/2.14? (as ?lib? is unspecified) Warning: unable to access index for repository http://essrc.hyogo-u.ac.jp/cran/bin/windows/contrib/2.14 Warning: unable to access index

i have R 2.14 version.and i have downloaded bayesQR package from following link http://cran.r-project.org/web/packages/bayesQR/index.html my OS is Windows7.i have downloaded Windows binary: bayesQR_1.3.zip file from above link.I am new to R. So please tell me what is the next step i have to do inorder to install the bayesQR package.pls reply me as quickly as possible. thanks in advance

sir, i am trying to install r package Bayesian quantile regression but i am facing with following problem which says forPlease select a CRAN mirror for use in this session --- Warning: unable to access index for repository http://cran.cnr.Berkeley.edu/bin/windows/contrib/2.6 Warning: unable to access index for repository http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/2.6 Error in

i know this is not about R. After applying quantile regression with t=0.5,0.6 on the data set WBC( Wisconsin Breast Cancer)with 678 observations and 9 independent variables(inp1,inp2,...inp9) and 1 dependent variable(op) i have got the following results for beta values. when t=0.5(median regression) beta values b1=0.002641,b2=0.045746,b3=0.

Error in `[.data.frame`(x, order(x, na.last = na.last, decreasing = decreasing)) : undefined columns selected during the execution of following r sequence of commands X<-subset(data,select=c(V1,V2,V3,V4,V5,V6,V7,V8,V9)) y<-subset(data,selcet=10) Data = list(y=y, X=X, p=.75) Prior = list(betabar=c(rep(0,ncol(X))),A=.01*diag(ncol(X))) Mcmc = list(R=100000, keep=10, step=.2) out <-

quantreg package is used. *fit1 results are* Call: rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9, tau = 0.15, data = wbc) Coefficients: (Intercept) inp1 inp2 inp3 inp4 inp5 -0.191528450 0.005276347 0.021414032 0.016034803 0.007510343 0.005276347 inp6 inp7 inp8 inp9 0.058708544

fit1<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit2<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.5,data=wbc) fit3<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit4<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc)