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install.packages("rgl", repos = "http://cran.rstudio.com/")
install.packages("ConsRank", repos = "http://cran.rstudio.com/")
library("ConsRank")


    

    




Installing package into ‘/usr/local/lib/R/site-library’
(as ‘lib’ is unspecified)
also installing the dependencies ‘httpuv’, ‘xtable’, ‘htmltools’, ‘sourcetools’, ‘later’, ‘promises’, ‘fastmap’, ‘miniUI’, ‘webshot’, ‘htmlwidgets’, ‘shiny’, ‘crosstalk’, ‘manipulateWidget’

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system("add-apt-repository -y ppa:marutter/rrutter")
system("add-apt-repository -y ppa:marutter/c2d4u")
system("apt-get update")
system("apt install -y r-cran-rstan")


    

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install.packages('ggmap')
install.packages('codetools')


    

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library("ggmap")


    

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options(repr.plot.width  = 3,
        repr.plot.height = 3)
Sys.setenv(USE_CXX14 = 1)
library("rstan") # observe startup messages
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)


    

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x <- seq(0, 2*pi, length.out=50)
plot(x, sin(x))


    

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stanmodelcode <- "
data {
    int<lower=0> N; 
    real y[N];
}

parameters {
    real mu; 

}

model {
    target += normal_lpdf(mu | 0, 10);
    target += normal_lpdf(y | mu, 1);
}"

y <- rnorm(20)
dat <- list(N = 20, y = y);

fit <- stan(model_code = stanmodelcode, model_name = "example",
            data = dat, iter = 2012, chains = 8, sample_file = 'norm.csv',
            verbose = FALSE,
            refresh = -1)

print(fit)


    

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plot(fit)


    

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y <- as.matrix(read.table('https://raw.github.com/wiki/stan-dev/rstan/rats.txt', header = TRUE))
x <- c(8, 15, 22, 29, 36)
xbar <- mean(x)
N <- nrow(y)
T <- ncol(y)
rats_fit <- stan(file = 'https://raw.githubusercontent.com/stan-dev/example-models/master/bugs_examples/vol1/rats/rats.stan')


    

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rats_fit


    

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plot(rats_fit)


    

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## load APA data set, full version
data(APAFULL)
## Emond and Mason Branch-and-Bound algorithm.
## If the number of object is higher than 20, EMCons function may work for several minutes.
## Use either QuickCons, DECOR, FASTcons or FASTDECOR instead
CR=EMCons(APAFULL)


    

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TR=tabulaterows(APAFULL)
CR2=FASTcons(TR$X,TR$Wk,maxiter=5)
CR3=QuickCons(TR$X,TR$Wk)
CR4=DECOR(TR$X,TR$Wk)
#####################################
### load sports data set
#data(sports)
### FAST algorithm
#CR=FASTcons(sports,maxiter=10)
#####################################
#######################################
### load Emond and Mason data set
#data(EMD)
### matrix X contains rankings
#X=EMD[,1:15]
### vector Wk contains frequencies
#Wk=EMD[,16]
### QUICK algorithm
#CR=QuickCons(X,Wk)
#######################################


    

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data(BU)
polyplot(BU[,1:3],Wk=BU[,4])


    

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