Candidate roadmap 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 and 9.6  MADLIB-944