npar.deriv <- function(filename,ages,birth.year){
  #################################################################################################################
  # Author: Daniela Kuruczova <daniela.kuruczova@mail.muni.cz>                                                    #
  # Purpose: Nonparametric mortality curve fitting via smoothing splines for                                      #
  #          data downloaded from http://www.mortality.org/                                                       #
  # Arguments: filename = path and name of file to use as data source                                             #
  #            ages = sequence of ages that will be used for the cohort                                           #
  #            birth year = birth year of the cohort                                                              #
  # Output: list with items:        "country": Country name                                                       #
  #                                 "ss.males": smoothing spline approximation of the male mortality curve        # 
  #                                 "deriv.males": first derivative of the male mortality curve                   #
  #                                 "ss.females": smoothing spline approximation of the female mortality curve    #
  #                                 "deriv.females": first derivative of the female mortality curve               #
  # Example of use: npar.deriv("Mx_1x1_Australia.txt",30:60,1954)                                                 #
  #                                                                                                               # 
  # 2018/11/21 Initial version (DK)                                                                               # 
  #################################################################################################################
  
  #Years to extract from data
  years <- birth.year+ages
  
  #Read data file
  dt <- read.table(filename,header=T,skip=2,na.strings='.')
  
  #Replace character + in Age
  dt$Age <- as.integer(gsub("+", "", as.character(dt$Age),fixed=T))
  
  #Get country name from filename
  country.name <- substr(filename, 8, regexpr('.txt', filename)[1] - 1)
  
  #Filter out data for specified cohort
  dt.to.use <- dt[dt$Age %in% ages & dt$Year %in% years & dt$Year-dt$Age==birth.year,]
  
  #Model calculation
  lambda <- 0.7
  #males
  s.M <- smooth.spline(dt.to.use$Age, dt.to.use$Male,spar=lambda)
  #females
  s.F <- smooth.spline(dt.to.use$Age, dt.to.use$Female,spar=lambda)

  age.new <- seq(min(ages),max(ages),length=200)
  dy.M <- predict(s.M, data.frame(Age=age.new), deriv = 1)
  dy.F <- predict(s.F, data.frame(Age=age.new), deriv = 1)
  s.M <- predict(s.M, data.frame(Age=age.new), deriv = 0)
  s.F <- predict(s.F, data.frame(Age=age.new), deriv = 0)

  output <- list(country=country.name,ss.males = s.M,deriv.males = dy.M,
                 ss.females=s.F, deriv.females = dy.F)
  return(output)
}