Clojure  
Programming Clojure by Stuart Halloway and Aaron Bedra. This is the second edition of the first book on Clojure, and is an excellent introduction.  
Clojure Programming by Chas Emerick, Brian Carper, and Christophe Grand. This is excellent introduction into Clojure language, with many examples.  
Clojure in Action by Amit Rathore. This book is an introduction into Clojure programming language.  
The Joy of Clojure: Thinking the Clojure Way by Michael Fogus and Chris Houser. This book describes more “advanced Clojure programming” and is great companion to any of aforementioned books.  
Clojure Data Analysis Cookbook – this book contains over 110 recipes to help you dive into the world of practical data analysis using Clojure, including how to use Incanter for this task. You can read sample chapter to get more information about this book.  
General statistics  
The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century By David Salsburg. An entertaining introduction to the history of statistics in the twentieth century, and how it influenced science.  
R books  
The R Book By Michael Crawley. This BIG book covers more of R’s features than anything else to date. It isn’t perfect, but it is a useful reference.  
Introductory Statistics with R (Statistics and Computing) By Peter Dalgaard. A really nice introduction to R.  
Software for Data Analysis: Programming with R (Statistics and Computing) By John Chambers. The best book on programming R, by the man behind S (the forerunner of R). Most books approach R as a statistical environment, this one talks about it as a programming language.  
Data Manipulation with R (Use R) By Phil Spector. Great book on using R to manipulate data.  
R Graphics (Computer Science and Data Analysis) By Paul Murrell. A great book covering both R’s traditional graphics system and the Lattice graphics system.  
Matrix algebra for statistics  
Matrices with Applications in Statistics (Duxbury Classic) By Franklin Graybill. A great introduction to the matrix algebra necessary for many statistical techniques. Old, but still great.  
Matrix Algebra: Theory, Computations, and Applications in Statistics (Springer Texts in Statistics) By James Gentle. Another excellent introduction to the matrix algebra needed for statistics, but with more emphasis on numerical methods than Graybill’s book.  
Bayesian statistics  
Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences) By Scott Lynch. This is the best introduction to using Bayesian methods and MCMC ever, and it has great example R code. The R and WinBugs code and data from the book are available at Scott Lynch’s homepage.  
Bayesian Data Analysis, Second Edition (Texts in Statistical Science) By Andrew Gelman. A next book after Scott Lynch’s, one of the standard texts. Also check out Andrew Gelman’s fantastic blog, Statistical Modeling, Causal Inference, and Social Science  
Bayesian Computation with R By Jim Albert. Another good introduction to Bayesian statistics with actual code examples in R. Many of the code examples rely on an R library by the author, which is available for download at the book’s website.  
Ordinal Data Modeling (Statistics for Social and Behavioral Sciences) By Valen Johnson & James Albert. Another good introduction to Bayesian (and classical) modeling, focusing on ordinal data in the social sciences. The Matlab code and data for the book’s examples are available online at James Albert’s homepage  
Machine learning  
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) by Hastie, Tibshirani, and Friedman. The classic book on statistical learning techniques by three of the best in the field. 

Pattern Recognition and Machine Learning (Information Science and Statistics) By Christopher Bishop. A great introduction to the fundamentals of machine learning techniques.  
Pattern Classification (2nd Edition) By Richard Duda. A great introduction and survey of machine learning techniques. A more applied approach than Bishop’s book with less of the theory behind the techniques.  
Causal modeling  
Structural Equations with Latent Variables By Ken Bollen. The classic text on structural equation modeling, and very well written.  
Causality: Models, Reasoning, and Inference By Judea Pearl. The best introduction to causal modeling with DAGs (directed acyclic graphs).  
FACTOR ANALYSIS By Richard Gorsuch. Old, but my favorite introductory text on factor analysis, and a classic.  
Data Visualization  
Visualizing Data: Exploring and Explaining Data with the Processing Environment By Ben Fry, this is a great introduction to Processing, and one that emphasizes data visualization. 

Processing: A Programming Handbook for Visual Designers and Artists By Casey Reas and Ben Fry, this is definitive reference for the Processing language. 

Learning Processing: A Beginner’s Guide to Programming Images, Animation, and Interaction (Morgan Kaufmann Series in Computer Graphics) By Daniel Shiffman, this is a fantastic introduction to graphics programming using Processing. 

Teaching statistics  
Teaching Statistics: A Bag of Tricks By Andrew Gelman and Deborah Nolan. A nice set of examples for teaching statistics to students. 
Search
Incanter
Clojure
 Asymmetrical View
 Clojure API documentation
 Clojure – Functional Programming for the JVM
 Clojure Getting Started Guide
 Clojure Google group
 Clojure on blip.tv
 Clojure Programming wikibook
 Clojure website
 Disclojure
 Joy of Clojure book site
 Learning Clojure wikibook
 Michael Fogus' blog
 Open Source Enabled
 Planet Clojure
 Programming Clojure book
 Sexpressions
Statistics
programming
visualization
Recent Comments
@liebke on Twitter
 Very happy I get to work with @alandipert again! “@LonoCloud welcomes @alandipert to the team! #awesome #cloud #clojure” 5 years ago
 RT @fogus: In homage to a seemingly great #ScalaDays 2012, I posted an interview with the brilliant @djspiewak bit.ly/I1jKcx #clo ... 6 years ago
 Eating breakfast at my hotel on the beach in La Jolla trying to remember why I moved away from here nearly 10 years ago. 6 years ago
 I'm really excited to be joining this amazing team! RT @LonoCloud: @LonoCloud welcomes @liebke to the team! #clojure #cloud #awesome 6 years ago
 RT @craigandera: Another new episode of the @thinkrelevance #podcast: @fogus on Himera. And lots of other stuff! bit.ly/GXXfW2 6 years ago

Recent Posts
 Incanter 1.5.7
 First preview of Incanter 2.0 (aka Incanter 1.9.0)
 Incanter 1.5.6 has been released
 Protocolbased dataset API in Incanter
 Planning breaking changes in the Incanter 2.0
 Incanter 1.5.5 has been released
 Incanter 1.5.4 has been released
 Incanter 1.5.2 (bugfix release)
 Incanter 1.5.1: Bugfix release
 Incanter 1.5.0 has been released!
 Hammock Driven Development Cheat Sheet
 An Illustrated guide to multicore parallelism in Clojure
 Incanter executables
 Reading and writing Excel (xls) files with Incanter
 Infix mathematical notation in Incanter
Del.icio.us/liebke
Topics
 Bayesian inference (1)
 benford's law (1)
 bootstrapping (1)
 chisquare test (2)
 classifiers (1)
 clojars.org (4)
 Clojure (39)
 clojureconj (1)
 compojure (2)
 conditional probability (2)
 congomongo (1)
 correlation (1)
 design (1)
 dirichlet distribution (1)
 emacs (1)
 Excel (1)
 FlightCaster (1)
 gaussnewton (1)
 goodnessoffit (1)
 Incanter (53)
 iran election (1)
 jFreeChart (6)
 labrepl (2)
 LaTeX (1)
 Leiningen (5)
 lineardiscriminateanalysis (1)
 machinelearning (1)
 Maven (1)
 mongodb (1)
 monte carlo simulation (1)
 montyhall (1)
 multinomial distribution (1)
 newtonraphson (1)
 open source (1)
 optimization (1)
 paredit (1)
 pca (1)
 permutation tests (2)
 plotting (7)
 probability (2)
 Processing (1)
 R (1)
 randomization (1)
 regression (1)
 release (1)
 resampling (2)
 Rincanter (1)
 roadmap (1)
 slime (1)
 statisticallearning (1)
 Statistics (9)
 swank (1)
 swingrepl (1)
 t test (2)
 tshirt (1)
 visualization (5)
 xls (1)