The rnn R package, describing how to implement from scratch the Recurrent Neural Network algorithm) in the R language is now available on CRAN. The rnn package can be installed simply from within R by running: install.packages('rnn') It does not have any dependencies, C++, Fortran, Java, or any other type of complexities. It is written purely in base R, so it should install without any issue on any R version.
The sigmoid package makes it easy to become familiar with the way neural networks work by demonstrating the key concepts using straightforward code examples. Installation The package can now be installed from CRAN using: {% highlight r %} install.packages(‘sigmoid’) # case sensitive! {% endhighlight %} Usage After installation, the package can be loaded using: {% highlight r %} library(sigmoid) {% endhighlight %} For information on using the package, please refer to the help files.
This is an example of how to build a Recurrent Neural Network in R. What we will do is generate two sets of random numbers, a and b, which we will sum up, the sum will be stored in the variable c. We then convert a and b into 8-bit binary values. E.g. 5 becomes: 0 0 0 0 0 1 0 1. We then sum the binary formatted a and b, bit by bit (i.e. column by column).
Below is an example of how to handcode a logit model. {% highlight r %} set.seed(123) n=100 N=1+rpois(n,5) X1=runif(n) X2=rexp(n) s=X2-X1-2 p=exp(s)/(1+exp(s)) vY=NULL for(i in 1:n){ Y=rbinom(1,prob=p[i],size=N[i]) vY=c(vY,Y) } db=data.frame(Y=vY,N=N,X1,X2) head(db,4) {% endhighlight %} {% highlight text %} Y N X1 X2 1 3 5 0.5999890 1.80312932 2 1 8 0.3328235 0.03005945 3 0 5 0.4886130 1.30344055 4 0 9 0.9544738 0.19979224 {% endhighlight %}
In this post we will discuss how to manually implement a Difference-in-Differences (DiD) estimator in R, using simulated data. {% highlight r %} reproducible random numbers set.seed(123) untreated and treated independent variable for period 0 xutr <- rnorm(1000, mean=5) xtr <- rnorm(1000, mean=1) create a data.frame with the dep. var., indep. var., time and id vars for period 0 dfutr <- data.frame(time = 0, id= 1:1000, y=xutr+15+rnorm(1000), x=xutr) dftr <- data.frame(time = 0, id=1001:2000, y=xtr +9+rnorm(1000), x=xtr ) df0 <- rbind(dfutr, dftr)