Candidate roadmap for remainder of 2016:

Predictive models

• Novelty detection using 1-class SVM  MADLIB-990
• Mixed effects modeling  MADLIB-987
• Factorization machines
• k-nearest neighbors (kNN)  MADLIB-927
• Geographically Weighted Regression (GWR)
• MCMC Probit and Logit regression

...

• Gaussian Mixture Model using Expectation Maximization (EM) algorithm
• Multi-layer Perceptron

...

Graph

• Shortest path  MADLIB-992
• Standard traversal

  ...

  • • depth first search

  ...

  • • breadth first search

  ...

  • • topological sort
  • One mode projection (converting a bi-partitite graph of user-item graph to user-user or item-item graph)
  • Connected components
  • Page rank
  • Hierarchical graph cut
  • Between-ness centrality
  • Minimum spanning tree

  Utilities

  • Path functions (phase 2)  MADLIB-977
  • Prediction metrics  MADLIB-907
  • Sessionization  MADLIB-909
  • Pivoting  MADLIB-908
  • Anonymization  MADLIB-911
  • URI tools  MADLIB-910
  • Stratified sampling  MADLIB-986

  Usability

  • Expand coverage for PivotalR
  • Expand coverage for PMML export
  • Interface improvement and consistency
  • Implement an interface using named parameters
  • Python API

  Performance and scalability

  • Work around PostgreSQL 1 GB field size limit  MADLIB-991

  Platform

  • Support for PostgreSQL 9.5  MADLIB-944