2.1 Binomial distribution (download)  
Plot the graphic of a Binomial distribution. Variables to customize:  prob < 0.1 # probability n < 10 # number of independent experiments barthickness < 20 # thickness of the graphical bars colour < rgb (1,0,0,0.5) # colour of the bars and line 

2.2 Poisson distribution (download)  
Plot the graphic of a Poisson distribution. Variables to customize:  lambda < 3 # lambda parameter (mean and variance) 

2.3 Uniform distribution (download)  
Plot the graphic of an Uniform distribution and draw a set of values from the distribution. Variables to customize:  xmin < 0.0 # minimum value 

2.4 Triangular distribution (download)  
Plot the graphic of a Triangular distribution and draw a set of values from the distribution. Variables to customize:  xmin < 14.5 # minimum value 

2.5 Normal distribution (download)  
Plot the graphic of a Normal distribution and draw a set of values from the distribution. Variables to customize:  media=5.0 # mean of the normal distribution 

2.6 Lognormal distribution (download)  
Plot the graphic of a Lognormal distribution and draw a set of values from the distribution. Variables to customize:  option < 1 # can be (1) mean and standard deviation are of the normal distribution, or (2) mean and standard deviation are of the lognormal values 

2.7 Normal distributions comparison (download)  
Compare several Normal distributions, with different means and variances, in the same graph. Variables to customize:  media < c(2,2,2,2,2) # mean of several normal distributions to compare 

2.8 Lognormal distribution comparison (download)  
Compare several Lognormal distributions, with different means and variances, in the same graph. Variables to customize:  media < c(2,2,2,2,2) # mean of several lognormal distributions to compare 

2.9 Power distribution (download)  
Plot the graphic of a Power distribution. Variables to customize:  xmin < 1 # minimum value of the distribution 

2.10 Quantitative empirical distribution (download)  
Plot the empirical distribution of a quantitative variable (simple histogram and cumulative histogram) and add the normal or lognormal distribution curve with the same mean and variance of the data. Variables to customize:  varlistindex < 5 # index of the GD frame quantitative variable 

2.11 Qualitative empirical distribution (download)  
Plot the empirical distribution of a qualitative variable (simple histogram and pseudocumulative histogram). Variables to customize:  varlistindex < 4 # index of the GTD frame variable 

2.12 Mix normal distributions (download)  
Use the Monte Carlo simulation to build a composite distribution mixing several normal distributions with different means and variances. Variables to customize:  nsims=500 # number of simulations (values to generate) 

2.13 Mix uniform distributions (download)  
Use the Monte Carlo simulation to build a composite distribution mixing several uniform distributions with different intervals. The composite distribution is based on a simplified formula of Benefits B = PV x Q – CE x Q – CF Variables to customize:  nsims=500 # number of simulations (values to generate) Q=10000 # value for parameter Q minPV=4 # minimum value for PV variable uniformly distributed maxPV=6 # maximum value for PV variable uniformly distributed minCE=1.5 # minimum value for CE variable uniformly distributed maxCE=3.5 # maximum value for CE variable uniformly distributed minCF=13000 # minimum value for CF variable uniformly distributed maxCF=15000 # maximum value for CF variable uniformly distributed valoravalia < 15 # evaluate the probability of being below this valoravalia nclasses < 10 # number of classes in the mixure histogram colour < rgb (0,0,1,0.5) # colour of the histogram bars and lines 