Daniel Gerlanc


Daniel Gerlanc has worked in analytics since 2006. He spent 5 years as a quantitative analyst with two Boston hedge funds before starting Enplus Advisors, Inc., a predictive analytics consultancy. At Enplus he works with clients in different industries to improve existing analytic processes and develop new ones. He has coauthored several open source R packages, published in peer-reviewed journals, and is active in local predictive analytics groups. He is a graduate of Williams College.


  • Kyle Campbell, Jeff Enos, Daniel Gerlanc, and David Kane. Backtests. R News, 7(1):36-41, April 2007
  • Daniel Gerlanc and Kris Kirby, bootES: An R Package for Bootstrap Confidence Intervals on Effect Sizes. Behavioral Research Methods, March 2013.
  • Iyengar A, Paulus JK, Gerlanc DJ, Maron JL. Detection and Potential Utility of C-Reactive Protein (CRP) in Saliva of Neonates. Frontiers in Pediatrics, November 2014.

Academic Statistical Consulting

  • Critchfield AC, Paulus JK, Farez R, Urato AC. “Abnormal Analyte Preeclampsia”: Do the second trimester maternal serum analytes help us to differentiate different types of preeclampsia? [Submitted to Pregnancy in Hypertension]
  • Paulus JK, Switkowski KM, Preston IR, Hill NS, Kari E. Roberts KE. Initiation of a Case-Control Study of Pulmonary Arterial Hypertension in Women. Poster presented at American Thoracic Society Annual Meeting, May 2012.

Invited Talks

R Packages

  • bootES: An easy to use interface for calculating bootstrap effect sizes in R.
  • portfolio: Classes for analysing and implementing equity portfolios.
  • backtest: The backtest package provides facilities for exploring portfolio-based conjectures about financial instruments (stocks, bonds, swaps, options, et cetera).


  • Genetic Association Studies – Teaching Assistant – Tufts University Medical School – 2013
  • Computation for RNA Sequencing – Teaching Assistant – Tufts University Medical School – 2013
  • Introduction to Data Science and Machine Learning – General Assembly Boston – July, 2013
  • Introduction to Python and Pandas for Data Analysis – Corporate Training – 2016 – Present