A library for machine learning and data analysis in R

R Machine Learning (Rml) is a machine learning library for the R programming language that I have begun to refactor from the work I did during my Master’s Thesis. Rml is not currently available to the public, but it will be once I have things cleaned up and documented. Stay tuned!!

Rml will include a number of machine learning algorithms with a focus on neural networks, including:

  • Feedforward networks
  • Elman style recurrent networks
  • Echo State Networks
  • Various optimization algorithms, such as Scaled Conjugate Gradients, RProp, Stochastic Gradient Descent and Alopex
  • Principal Components Analysis
  • Linear and Quadratic Discriminant Analysis
  • Linear Logistic Regression
  • Autoregressive Models



A feedforward network trained with ALOPEX learning to fit a noisy standing sine wave.

A feedforward network trained with ALOPEX learning to fit the exclusive or (XOR) function.

Feedforward Neural Network fitting a sinc function.
A two-layer feedforward network trained using Scaled Conjugate Gradients (SCG) to fit a noisy sinc function.

An Echo State Network (ESN) with 500 hidden units solving the temporal XOR problem with a shift of 6. This problem requires both a non-linear transfer function as well as state/memory.