if(!require(SPEI)) devtools::install_github('sbegueria/SPEI@v1.7.1') # use 1.7.1
#> Loading required package: SPEI
#> Loading required package: lmomco
#> Loading required package: parallel
#> Loading required package: ggplot2
#> # Package SPEI (1.7) loaded [try SPEINews()].

library(WASP)
library(synthesis)
#> 
#> Attaching package: 'synthesis'
#> The following objects are masked from 'package:WASP':
#> 
#>     data.gen.ar1, data.gen.ar4, data.gen.ar9, data.gen.HL,
#>     data.gen.Rossler, data.gen.SW, data.gen.tar1, data.gen.tar2

library(ggplot2)
require(SPEI)
library(FNN)
#> 
#> Attaching package: 'FNN'
#> The following object is masked from 'package:WASP':
#> 
#>     knn
library(waveslim)
#> 
#> waveslim: Wavelet Method for 1/2/3D Signals (version = 1.8.4)
library(cowplot)
library(gridGraphics)
#> Loading required package: grid

# data generation
x <- arima.sim(list(order = c(1, 0, 0), ar = 0.6), n = 512)
# x <- as.numeric(scale(data.gen.Rossler(time = seq(0, 50, length.out = 512))$x, scale=F))

# Daubechies wavelets
for (wf in c("haar", "d4", "d8", "d16")) {
  print(paste0("Wavelet filter: ", wf))
  #----------------------------------------------------------------------------
  # wavelet family, extension mode and package
  # wf <- "haar" # wavelet family D8 or db4
  boundary <- "periodic"
  if (wf != "haar") v <- as.integer(readr::parse_number(wf) / 2) else v <- 1

  # Maximum decomposition level J
  n <- length(x)
  J <- ceiling(log(n / (2 * v - 1)) / log(2)) - 1 # (Kaiser, 1994)

  cov <- rnorm(J + 1, sd = 2)
  Vr <- as.numeric(cov / norm(cov, type = "2") * sd(x))
  #----------------------------------------------------------------------------
  # DWT-MRA
  #print("-----------DWT-MRA-----------")
  x.mra <- waveslim::mra(x, wf = wf, J = J, method = "dwt", boundary = boundary)
  x.mra.m <- matrix(unlist(x.mra), ncol = J + 1)

  x.n <- scale(x.mra.m) %*% Vr
  var(x.n) - var(x)

  message(paste0("Additive decompostion: ", isTRUE(all.equal(as.numeric(x), rowSums(x.mra.m)))))
  message(paste0("Variance decompostion: ", isTRUE(all.equal(var(x), sum(apply(x.mra.m, 2, var))))))

  #----------------------------------------------------------------------------
  # MODWT
  #print("-----------MODWT-----------")
  x.modwt <- waveslim::modwt(x, wf = wf, n.levels = J, boundary = boundary)
  x.modwt.m <- matrix(unlist(x.modwt), ncol = J + 1)

  x.n <- scale(x.modwt.m) %*% Vr
  var(x.n) - var(x)

  message(paste0("Additive decompostion: ", isTRUE(all.equal(as.numeric(x), rowSums(x.modwt.m)))))
  message(paste0("Variance decompostion: ", isTRUE(all.equal(var(x), sum(apply(x.modwt.m, 2, var))))))

  #----------------------------------------------------------------------------
  # a trous
  #print("-----------AT-----------")
  x.at <- at.wd(x, wf = wf, J = J, boundary = boundary)
  x.at.m <- matrix(unlist(x.at), ncol = J + 1)

  # x.mra.modwt <- waveslim::mra(x,wf=wf, J=J, method="modwt", boundary=boundary)
  # x.mra.modwt <- matrix(unlist(x.mra.modwt), ncol=J+1)
  #
  # print(sum(abs(x.at.m-x.mra.modwt)))

  message(paste0("Additive decompostion: ", isTRUE(all.equal(as.numeric(x), rowSums(x.at.m)))))
  message(paste0("Variance decompostion: ", isTRUE(all.equal(var(x), sum(apply(x.at.m, 2, var))))))

  if (isTRUE(all.equal(x.at.m, x.modwt.m))) {
    message(paste0("AT and MODWT is equivalent using the", wf, "!"))
  }
}
#> [1] "Wavelet filter: haar"
#> Additive decompostion: TRUE
#> Variance decompostion: TRUE
#> Additive decompostion: TRUE
#> Variance decompostion: TRUE
#> Additive decompostion: TRUE
#> Variance decompostion: TRUE
#> AT and MODWT is equivalent using thehaar!
#> [1] "Wavelet filter: d4"
#> Additive decompostion: TRUE
#> Variance decompostion: TRUE
#> Additive decompostion: FALSE
#> Variance decompostion: TRUE
#> Additive decompostion: TRUE
#> Variance decompostion: FALSE
#> [1] "Wavelet filter: d8"
#> Additive decompostion: TRUE
#> Variance decompostion: TRUE
#> Additive decompostion: FALSE
#> Variance decompostion: TRUE
#> Additive decompostion: TRUE
#> Variance decompostion: FALSE
#> [1] "Wavelet filter: d16"
#> Additive decompostion: TRUE
#> Variance decompostion: TRUE
#> Additive decompostion: FALSE
#> Variance decompostion: TRUE
#> Additive decompostion: TRUE
#> Variance decompostion: FALSE

p.list <- NULL
wf.opts <- c("d16", "haar")
for (k in seq_along(wf.opts)) {
  # data generation
  x <- arima.sim(list(order = c(1, 0, 0), ar = 0.6), n = 128)

  #----------------------------------------------------------------------------
  # wavelet family, extension mode and package
  wf <- wf.opts[k] # wavelet family D8 or db4
  boundary <- "periodic"
  if (wf != "haar") v <- as.integer(readr::parse_number(wf) / 2) else v <- 1

  # Maximum decomposition level J
  n <- length(x)
  J <- ceiling(log(n / (2 * v - 1)) / log(2)) - 1 # (Kaiser, 1994)

  limits.x <- c(0, n)
  limits.y <- c(-3, 3)
  #----------------------------------------------------------------------------
  # DWT-MRA
  x.mra <- waveslim::mra(x, wf = wf, J = J, method = "dwt", boundary = boundary)
  x.mra.m <- matrix(unlist(x.mra), ncol = J + 1)

  p1 <- mra.plot(x, x.mra.m, limits.x, limits.y,
    ylab = "X", col = "red", type = "details",
    main = paste0("DWT-MRA", "(", wf, ")"), ps = 12
  )
  # p1 <- recordPlot()

  #----------------------------------------------------------------------------
  # MODWT
  x.modwt <- waveslim::modwt(x, wf = wf, n.levels = J, boundary = boundary)
  x.modwt.m <- matrix(unlist(x.modwt), ncol = J + 1)

  p2 <- mra.plot(x, x.modwt.m, limits.x, limits.y,
    ylab = "X", col = "red", type = "coefs",
    main = paste0("MODWT", "(", wf, ")"), ps = 12
  )

  #----------------------------------------------------------------------------
  # a trous
  x.at <- at.wd(x, wf = wf, J = J, boundary = boundary)
  x.at.m <- matrix(unlist(x.at), ncol = J + 1)

  p3 <- mra.plot(x, x.at.m, limits.x, limits.y,
    ylab = "X", col = "red", type = "coefs",
    main = paste0("AT", "(", wf, ")"), ps = 12
  )

  p.list[[k]] <- list(p1, p2, p3)
}

#----------------------------------------------------------------------------
# plot and save
cowplot::plot_grid(
  plotlist = p.list[[1]], ncol = 3, labels = c("(a)", "(b)", "(c)"),
  label_size = 12
)

#----------------------------------------------------------------------------
# plot and save
cowplot::plot_grid(
  plotlist = p.list[[2]], ncol = 3, labels = c("(a)", "(b)", "(c)"),
  label_size = 12
)

if (FALSE) {
  ### Synthetic example
  # data generation
  set.seed(2020)
  sample <- 512
  # frequency, sampled from a given range
  fd <- c(3, 5, 10, 15, 25, 30, 55, 70, 95)
  # data <- WASP::data.gen.SW(nobs=sample,fp=25,fd=fd)
  data <- WASP::data.gen.SW(nobs = sample, fp = c(15, 25, 30), fd = fd)

  # ts = data.gen.Rossler(time = seq(0, 50, length.out = sample))
  # data <- list(x=ts$z, dp=cbind(ts$x, ts$y))
} else {
  ### Real-world example
  data("obs.mon")
  data("rain.mon")

  if (TRUE) { # SPI12 as response
    SPI.12 <- SPEI::spi(rain.mon[, 5], scale = 12)$fitted
    x <- window(SPI.12, start = c(1950, 1), end = c(2009, 12))
    dp <- window(obs.mon, start = c(1950, 1), end = c(2009, 12))
  } else { # rainfall as response
    x <- window(rain.mon[, 5], start = c(1950, 1), end = c(2009, 12))
    dp <- window(obs.mon, start = c(1950, 1), end = c(2009, 12))
  }
  data <- list(x = x, dp = dp)
  sample <- length(x)
}

# plot.ts(cbind(data$x,data$dp))

tab.list <- list()
mode.opts <- c("MRA", "MODWT", "AT")
for (mode in mode.opts) {
  print(mode)

  # cov.opt <- switch(2,"auto","pos","neg")
  if (mode == "MRA") {
    method <- switch(1,"dwt", "modwt")
  }

  # wavelet family, extension mode and package
  # wf <- switch(mode, "MRA"="haar", "MODWT"="haar", "AT"="haar")
  wf <- "haar"
  pad <- "zero"
  boundary <- "periodic"
  if (wf != "haar") v <- as.integer(readr::parse_number(wf) / 2) else v <- 1

  # Maximum decomposition level J
  n <- sample
  J <- ceiling(log(n / (2 * v - 1)) / log(2)) - 1 # (Kaiser, 1994)

  tab <- NULL
  for (cov.opt in c("auto", "pos", "neg")[1]) {
    # variance transform - calibration
    if (mode == "MRA") {
      dwt <- dwt.vt(data, wf, J, method, pad, boundary, cov.opt)
    } else if (mode == "MODWT") {
      dwt <- modwt.vt(data, wf, J, boundary, cov.opt)
    } else {
      dwt <- at.vt(data, wf, J, boundary, cov.opt)
    }

    # optimal prediction accuracy
    opti.rmse <- NULL
    dp.RMSE <- NULL
    dp.n.RMSE <- NULL
    S <- dwt$S
    ndim <- ncol(S)
    for (i in 1:ndim) {
      x <- dwt$x
      dp <- dwt$dp[, i]
      dp.n <- dwt$dp.n[, i]

      # ts.plot(cbind(dp,dp.n), col=1:2)

      dp.RMSE <- c(dp.RMSE, sqrt(mean(lm(x ~ dp)$residuals^2)))
      dp.n.RMSE <- c(dp.n.RMSE, sqrt(mean(lm(x ~ dp.n)$residuals^2)))

      # small difference due to the reconstruction
      opti.rmse <- c(opti.rmse, sqrt((n - 1) / n * (var(x) - sum(S[, i]^2) * var(dp) / var(dp.n))))
      # opti.rmse <- c(opti.rmse, sqrt((n-1)/n*(var(x)-sum(S[,i]^2))))
    }

    tab <- rbind(tab, data.frame(cov.opt, var=1:ndim, dp.RMSE, dp.n.RMSE, opti.rmse))
  }

  colnames(tab) <- c("Sign of covariance", "Variable", "Std", "VT", "Optimal")
  tab.list[[length(tab.list) + 1]] <- tab
}

# print(tab.list)

kable(tab.list[[1]][,-1], caption = "Optimal RMSE using DWT-based VT",
      booktabs = T, align = "c", digits = 3) %>%
kable_styling("striped", position = "center", full_width = FALSE)  %>%
collapse_rows(columns = 1, valign = "middle")

kable(tab.list[[2]][,-1], caption = "Optimal RMSE using MODWT/AT-based VT",
      booktabs = T, align = "c", digits = 3) %>%
kable_styling("striped", position = "center", full_width = FALSE)  %>%
collapse_rows(columns = 1, valign = "middle")

#-------------------------------------------------------------------
if (FALSE) {
  set.seed(2020)
  ### synthetic example - Rossler
  sample <- 10000
  s <- 0.1
  ts.list <- list()
  for (i in seq_along(s)) {
    ts.r <- data.gen.Rossler(a = 0.2, b = 0.2, w = 5.7, start = c(-2, -10, 0.2), time = seq(0, 50, length.out = sample))

    # add noise
    ts.r$x <- ts(ts.r$x + rnorm(n = sample, mean = 0, sd = s[i]))
    ts.r$y <- ts(ts.r$y + rnorm(n = sample, mean = 0, sd = s[i]))
    ts.r$z <- ts(ts.r$z + rnorm(n = sample, mean = 0, sd = s[i]))

    ts.list[[i]] <- ts.r
  }

  data.list <- lapply(ts.list, function(ts) list(x = ts$z, dp = cbind(ts$x, ts$y)))

  lab.names <- c("x", "y")
  xlim<- c(0,n); ylim <- c(-55, 55)
} else {

  ### Real-world example
  data("obs.mon")
  data("rain.mon")

  SPI.12 <- SPEI::spi(rain.mon[, 5], scale = 12)$fitted
  x <- window(SPI.12, start = c(1950, 1), end = c(2009, 12))
  dp <- window(obs.mon, start = c(1950, 1), end = c(2009, 12))

  data.list <- list(list(x = x, dp = dp))
  sample <- length(x)

  lab.names <- colnames(obs.mon)
  xlim<- NULL; ylim <- NULL
}

#-------------------------------------------------------------------
p.list <- list()
dp.list <- list()
if (wf != "haar") mode.opts <- c("MRA", "MODWT", "AT")[1:3] else mode.opts <- c("MRA", "MODWT","AT")[1:2]

for (mode in mode.opts) {
  cov.opt <- switch(1,"auto","pos","neg")
  flag <- switch(1,"biased", "unbiased")
  
  if (mode == "MRA") {
    method <- switch(1,"dwt","modwt")
  }

  # wavelet family, extension mode and package
  # wf <- switch(mode, "MRA"="haar", "MODWT"="haar", "AT"="haar")
  wf <- "d16"
  pad <- "zero"
  boundary <- "periodic"
  if (wf != "haar") v <- as.integer(readr::parse_number(wf) / 2) else v <- 1

  # Maximum decomposition level J
  n <- sample
  J <- ceiling(log(n / (2 * v - 1)) / log(2)) - 1 # (Kaiser, 1994)
  # J <- floor(log(n/(2*v-1))/log(2))

  # variance transform - calibration
  if (mode == "MRA") {
    dwt.list <- lapply(data.list, function(x) dwt.vt(x, wf, J, method, pad, boundary, cov.opt, flag))
  } else if (mode == "MODWT") {
    dwt.list <- lapply(data.list, function(x) modwt.vt(x, wf, J, boundary, cov.opt, flag))
  } else {
    dwt.list <- lapply(data.list, function(x) at.vt(x, wf, J, boundary, cov.opt, flag))
  }

  for (j in 1:length(dwt.list)) {
    dwt <- dwt.list[[j]]

    par(
      mfrow = c(ncol(dwt$dp), 1), mar = c(0, 2.5, 2, 1),
      oma = c(2, 1, 0, 0), # move plot to the right and up
      mgp = c(1.5, 0.5, 0), # move axis labels closer to axis
      pty = "m", bg = "transparent",
      ps = 12
    )

    # plot(dwt$x, type="l", xlab=NA, ylab="SPI12", col="red")
    # plot(dwt$x, type="l", xlab=NA, ylab="Rain", col="red")
    for (i in 1:ncol(dwt$dp)) {
      ts.plot(cbind(dwt$dp[, i], dwt$dp.n[, i]),
        xlab = NA, ylab = paste0(lab.names[i]),
        xlim = xlim, ylim = ylim,
        col = c("black", "blue"), lwd = c(1, 2)
      )
    }

    p.list[[length(p.list) + 1]] <- recordPlot()

    dp.list[[length(dp.list) + 1]] <- dwt$dp.n
  }
}

#----------------------------------------------------------------------------
# plot and save
fig <- cowplot::plot_grid(plotlist = p.list, nrow = 1, labels = c("(a)", "(b)", "(c)"))
fig

#-------------------------------------------------------------------
### Real-world example
data("obs.mon")
data("rain.mon")
op <- par()
station.id <- 5
lab.names <- colnames(obs.mon)[c(1, 3, 4, 5, 7)]

if (TRUE) { # SPI12 as response
  SPI.12 <- SPEI::spi(rain.mon, scale = 12)$fitted
  x <- window(SPI.12, start = c(1950, 1), end = c(2009, 12))
  dp <- window(obs.mon[, lab.names], start = c(1950, 1), end = c(2009, 12))
} else { # rainfall as response
  x <- window(rain.mon, start = c(1950, 1), end = c(2009, 12))
  dp <- window(obs.mon[, lab.names], start = c(1950, 1), end = c(2009, 12))
}

data.list <- lapply(station.id, function(id) list(x = x[, id], dp = dp))


ylim <- data.frame(
  GPH = c(700, 900), TDP700 = c(5, 25), TDP500 = c(5, 25), EPT = c(300, 330),
  UWND = c(-5, 25), VWND = c(-5, 10), MSLP = c(-1, 1)
)[c(1, 3, 4, 5, 7)]

#-------------------------------------------------------------------
p.list <- list()
RMSE <- NULL
mode.opts <- c("MRA", "MODWT", "AT")[1:2]
for (mode in mode.opts) {
  cov.opt <- switch(1,
    "auto",
    "pos",
    "neg"
  )
  if (mode == "MRA") {
    method <- switch(1,
      "dwt",
      "modwt"
    )
  }

  # wavelet family, extension mode and package
  wf <- switch(mode,"MRA" = "d4","MODWT" = "haar","AT" = "haar")
  pad <- "zero"
  boundary <- "periodic"
  if (wf != "haar") v <- as.integer(readr::parse_number(wf) / 2) else v <- 1

  # Maximum decomposition level J
  n <- nrow(x)
  J <- ceiling(log(n / (2 * v - 1)) / log(2)) - 1 # (Kaiser, 1994)

  # high order variance transformation
  dwt.list <- lapply(data.list, function(data) stepwise.VT(data, mode = mode, wf = wf, J=J))

  for (j in seq_len(length(dwt.list))) {
    dwt <- dwt.list[[j]]
    cpy <- dwt$cpy

    MSE <- NULL
    for (i in seq_len(length(cpy))) {
      m1 <- sqrt(FNN::knn.reg(train = dwt$dp[, 1:i], y = dwt$x)$PRESS / n)
      m2 <- sqrt(FNN::knn.reg(train = dwt$dp.n[, 1:i], y = dwt$x)$PRESS / n)

      MSE <- rbind(MSE, c(m1, m2))
    }

    RMSE <- cbind(RMSE, MSE)

    par(
      mfrow = c(length(cpy), 1), mar = c(0, 4, 2, 1),
      oma = c(2, 1, 0, 0), # move plot to the right and up
      mgp = c(1.5, 0.5, 0), # move axis labels closer to axis
      pty = "m", bg = "transparent",
      ps = 8
    )

    # plot(dwt$x, type="l", xlab=NA, ylab="SPI12", ylim=c(-3,3),col="red")
    # plot(dwt$x, type="l", xlab=NA, ylab="Rain", col="red")
    for (i in seq_len(length(cpy))) {
      ts.plot(dwt$dp[, i], dwt$dp.n[, i],
        xlab = NA, ylab = paste0(lab.names[cpy[i]]), # ylim=ylim[,i],
        col = c("black", "blue"), lwd = c(1, 2)
      )
    }

    p.list[[length(p.list) + 1]] <- recordPlot()
  }
}
#> [1] "Variance difference between transformed\n                        and original series by percentage: 17.2263230629905"
par(op)
#-------------------------------------------------------------------
# plot and save
cowplot::plot_grid(plotlist = p.list, nrow = 1, labels = c("(a)", "(b)", "(c)"))

#-------------------------------------------------------------------
# RMSE when more predictors are included
tab1 <- round(RMSE, 3)
tab1 <- cbind(1:nrow(tab1), tab1)
colnames(tab1) <- c("No. of Predictors", rep(c("Original", "Transformed"), length(mode.opts)))

kable(tab1, caption = "Comparison of prediction accuracy using Std and SVT", booktabs = T) %>%
  kable_styling(latex_options = c("HOLD_position"), position = "center", full_width = FALSE)  %>%
  #  add_header_above(c(" " = 1, "DWT-MRA" = 2, "MODWT" = 2, "AT" = 2))
  add_header_above(c(" " = 1, "DWT-MRA" = 2, "MODWT/AT" = 2))

library(wordcloud)
library(wordcloud2)
library(RColorBrewer)

library(tm) # for text mining
library(dplyr)
library(readr)
library(ggplot2)

#the folder where downloaded text are
filepath <- "./data/journal" 

file.list <- list.files(filepath,full.names=TRUE)

file.list.JoH <- file.list[grep("J HYDROL",file.list)]


tmp.title=tmp.keyword <- NULL
for (tmp.file in file.list.JoH) {
  
  file.txt <- sapply(list.files(tmp.file,full.names=TRUE), function(path) readLines(path))
  
  tmp.title <- c(tmp.title, sapply(file.txt, function(txt) txt[!is.na(stringr::str_extract(txt, "TI"))]))
  tmp.keyword <- c(tmp.keyword, sapply(file.txt, function(txt) txt[!is.na(stringr::str_extract(txt, "DE"))]))
  
}


text.keyword <- do.call(rbind, tmp.keyword)
text.title <- do.call(rbind, tmp.title)

# Clean the text data
# gsub("https\\S*", "", text)
# gsub("@\\S*", "", text)
# gsub("  title = ", "", text)
# gsub("[\r\n]", "", text)
# gsub("[[:punct:]]", "", text)
text.title <- gsub("‐", " ", text.title)

text.title.n <- iconv(text.title, "UTF-8", "UTF-8",sub='')
text1 <- gsub("\\}.*", "}", gsub(".*\\{", "{", text.title.n))

# Create a corpus  
docs <- Corpus(VectorSource(text1))

# Clean the text data
docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs1 <- tm_map(docs, content_transformer(tolower))
docs2 <- tm_map(docs1, removeWords, stopwords("english"))

# Create a document-term-matrix
dtm <- TermDocumentMatrix(docs2) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 

df <- data.frame(word = names(words),freq=words)
summary(df)
head(df)

ggplot(df[df$freq>quantile(df$freq,0.99),]) + 
  geom_col(aes(x=word, y=freq)) + 
  theme_bw()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

###with wordcloud package 
set.seed(2021-04-28) # for reproducibility 

df1 <- df
head(df1)

###remove unwanted words manually
df.text1 <- df1[!(df1$word %in% c("–","using","and","for","of")),]

max.words <- quantile(nrow(df), 0.01)
max.words <- 200
wordcloud(words = df.text1$word, freq = df.text1$freq, min.freq = 1, max.words=max.words, random.order=FALSE, 
          rot.per=0.35, colors=rev(brewer.pal(8, "Dark2")))

###with wordcloud2 package 
set.seed(1234) # for reproducibility 

df2 <- df
head(df2)

df.text2 <- df2[!(df2$word %in% c("–","using","and","for","of")),]

wordcloud2(data=df.text2, size=0.35, color='random-dark',shape = 'circle',ellipticity=1)
wordcloud2(data=df.text2, size=0.35, color='random-dark',shape = 'square',ellipticity=1)
wordcloud2(data=df.text2, size = 0.35, shape = 'pentagon',ellipticity=1)

pandoc --extract-media=. --wrap=none -s input.docx -t latex –o output.tex

%equation 
\begin{equation}
  \label{eq:eq1}
  x + y = z
\end{equation}

%table 
\begin{table}[H]
  \caption{Table 1\label{tab:tab1}}
  \begin{tabular}{ll}
  \toprule
  Tabe Head
  \midrule
  text
  \bottomrule
  \end{tabular}
\end{table}

%figure
\begin{figure}
  \caption{Figure 1\label{fig:fig1}}
  \includegraphics[width=\linewidth]{Fig.jpg}
\end{figure}

Cite by: \ref{eq:eq1}; \ref{tab:tab1}; \ref{fig:fig1}

%in the preamble
\bibliographystyle{plain}

%Where the bibliography will be printed
\bibliography{name of .bib file}


@book{RN1,
   author = {Gastel, Barbara and Day, Robert A.},
   title = {How to Write and Publisher a Scientific Paper},
   publisher = {Cambridge University Press},
   address = {New York, USA},
   year = {2017},
   type = {Book}
}

Cite by: \cite{RN1}

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%Elsevier
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