Here is the latest bag of tweets^*, which covers February 2014.

(*) These are interesting news that I found on Twitter and that I archive periodically.

• dataJujitsu: I just set up a twitter bot to collect research papers using Electronic Medical Records: @EMR_research #EMR #caredata #medicalresearch (25 Feb)
• arnicas: More algorithm vis! RT @d3visualization: .@bflyon visualizations from the last year http://t.co/ayjD10bewB (25 Feb)
• quandl: #Free book: Mining of Massive Datasets, 2nd Edition by @jure, @anand_raj & Jeff Ullman via @KDnuggets http://t.co/uOOvouuM0O #DataMining (25 Feb)
• twiecki: New blog post: “Easily distributing a parallel #IPython Notebook on a cluster” http://t.co/RhBnBEIWU5 (25 Feb)
• JFPuget: @lauramclay Hindsight here https://t.co/GlPqEZGKW7 (25 Feb)
• yoavram: Great review - Adaptive evolution: evaluating empirical support for theoretical predictions. http://t.co/wAkpuwdcFY (24 Feb)
• johnmyleswhite: Really great tutorial by Jason Merrill about writing algorithms that work correctly with floating point numbers: http://t.co/qlsSH47uNe (24 Feb)
• genetics_blog: CGAT: computational #genomics analysis toolkit http://t.co/xwO9n10VlS (24 Feb)
• fonnesbeck: Materials for this afternoon’s tutorial on statistical data analysis in Python are here: http://t.co/aahinBjq4b #PyTennessee #PyTN2014 (24 Feb)
• JonesZM: to explain or predict? a very cool paper http://t.co/lTruhg03jE (24 Feb)
• furukama: @ianozsvald Ah, so many IPython Notebooks to try out ;-) http://t.co/4TqDnRjWTf #PyData #sklearn (24 Feb)
• Statistics_Man: Please Retweet! BMJ Statistics Endgames (Toll Free Link). Nested case-control studies: advantages and disadvantages. http://t.co/CIrJjzuy7x (23 Feb)
• Paul_Kinlan: Auto-deploying Jekyll via Github my way :) http://t.co/9w42BvgSgJ (23 Feb)
• Rbloggers: High Dimensional Biological Data Analysis and Visualization: (This article was first published on … http://t.co/OVOSjbwH7H #rstats (23 Feb)
• trent_hauck: An introduction to Information Retrieval: http://t.co/NoCueFJ6v0. (23 Feb)
• moorejh: most important scientific theories can be summed up with #visualization http://t.co/QZFa239rwR #scichat #infovis http://t.co/5j3kkf8FzR (23 Feb)
• johnmyleswhite: I’ve been saying that we need a “Counterexamples in Statistics” book for a while. So I started a collaborative book: https://t.co/u2foeBqsle (23 Feb)
• algoriffic: Generating histograms in Postgres/psql. http://t.co/l8bqg1yWNW (23 Feb)
• newsycombinator: A Geometric Review of Linear Algebra [pdf] http://t.co/yb8xWE6WR2 (22 Feb)
• elazungu: list of python tools for machine learning & deep learning http://t.co/sCjLnfHXkb (22 Feb)
• jenjpan: Running an Intro to Python for Text Analysis workshop today at MIT PoliSci, materials here: http://t.co/9Ag1BstxjI (22 Feb)
• rOpenSci: taxize tutorial for v0.2 updated on our site http://t.co/67OaOKcnQA #rstats (21 Feb)
• ChrisDiehl: Outlier Analysis - Interesting book from Springer - http://t.co/TGckXsTegU HT @AmanQA (21 Feb)
• mayoerrorstat: STEPHEN SENN: Fisher’s alternative to the alternative http://t.co/PicwT15ZiE (21 Feb)
• hye_jeong_kim: An Illustration of Multilevel Factor Analysis - Journal of Personality Assessment - Volume 84, Issue 2 http://t.co/LeMlfJ1Y2s (21 Feb)
• vandy_biostat: Why Python uses 0-based indexing http://t.co/qEAi047f2K (21 Feb)
• twiecki: If you’re reading Kruschke’s “Doing Bayesian Data Analysis”, here is an early effort to port the samples to pymc: https://t.co/M4iQcXjyMr (20 Feb)
• GSwithR: mages’ blog: Adding labels within lattice panels by group: http://t.co/BJJTgIgAoD (19 Feb)
• twiecki: Student-t Processes as Alternatives to Gaussian Processes http://t.co/AqNWt0FIe4 looks cool! (19 Feb)
• genetics_blog: Introduction to Python for Statistical Learning http://t.co/ug7q6izPv5 #pydata #rstats (18 Feb)
• DataCamp_com: First #Coursera Course on Data Science making use of @DataCamp_com for interactive lab complements http://t.co/9JyOeBYAqe #openscience (18 Feb)
• bbatsov: Resolving #git merge conflicts with #magit and #ediff https://t.co/0P4MBHXSOf #emacs (18 Feb)
• cjohnson318: Linear Regression with Python http://t.co/ohhsIyAMNw (18 Feb)
• zentree: @mja Also in this paper I have some INLA code in script 1; I’m not sure if it would be of any use. http://t.co/4MbE7H0Rnz (18 Feb)
• triadsou: “A Brief Survey of Modern Optimization for Statisticians - Lange - 2014 - International Statistical R…” http://t.co/HghvQIX5o8 #Optimization (18 Feb)
• kaz_yos: Submitted my first #rstat package to #CRAN. It create Table 1, common in med research https://t.co/Kf8Mn43zGT http://t.co/Jxs4q3Yxi2 (18 Feb)
• jnvmiles: R added more functions in 2012 than SAS has in total. http://t.co/cIXGyXPGhV #rstats (18 Feb)
• hmason: A great one by @simplystats: On the scalability of statistical procedures: why the p-value bashers just don’t get it. http://t.co/rsVqSvgw6S (18 Feb)
• cbahlai: @ucfagls Any feelings about zero-adjusting in NMDS (ie:http://t.co/X9KBDP9p7J). Collegue REALLY wants to use it, I’m less convinced (18 Feb)
• siah: BONFERRONI AND STABILITY OF MULTIPLE TESTING http://t.co/Hj0pIWemys (18 Feb)
• RDub2: Data Analytics Takes on Medication Management http://t.co/vRtY59tAWF (18 Feb)
• unixandperl: This can be super useful sometimes RT @UnixToolTip: Show line endings in a file: cat -E (18 Feb)
• ramnath_vaidya: Sochi Medal Tally - rCharts + OpenCPU + NVD3 #rstats http://t.co/CvzUHfgOmC @OpenCPU #nvd3 http://t.co/14NNvWJfdp (18 Feb)
• ModusGolems: @divbyzero I write a maths/computer science at http://t.co/etGU7MQjV6 (18 Feb)
• edyong209: Nice set of tributes to Daniel Kahneman’s influence http://t.co/FWXkle4OYh (17 Feb)
• cemerick: “white paper” & discussion re: GPLv3 & other interesting licensing issues (e.g. copyright assignment) linux-kernel ML http://t.co/nWELDvhA19 (17 Feb)
• borkdude: The O’Reilly #clojure cookbook is awesome and what’s more awesome, it can be obtained here for free: https://t.co/zzjCrXJBFw #opensource (17 Feb)
• jochmann: Like an animal! That’s how Apple treats customers who would like to continue using email. @drdrang http://t.co/oo7NTyMsBv (17 Feb)
• tomstafford: See also our paper in @TrendsCognSci “Brain network: social media and the cognitive scientist” http://t.co/WFutFpqYli (17 Feb)
• selenakyle: interesting article: effects of prolonged stress on self-control, performance http://t.co/kvv5bf0oq8 (17 Feb)
• HNTweets: Siteup.io – platform for static web sites backed by Git [pre registration]: http://t.co/PHJ4AW26NV Comments: https://t.co/DrqHW8IuET (17 Feb)
• blattnerma: Authorship Analysis based on Data Compression #machinelearning #IR http://t.co/LQICn8mIc9 (17 Feb)
• davideagleman: Douglas Hofstadter on why Watson and Siri are not meaningful examples of artificial intelligence. via @PopMech http://t.co/aHJJ2I1L0M (17 Feb)
• ChrisBeeley: I profoundly love LibreOffice. Here’s some analysis of why it’s so great http://t.co/NblD49L2dy (16 Feb)
• marcosluis2186: Wow, amazing article about Migrating from #MongoDB to #Cassandra http://t.co/SGYNikb5sK thx to @Xorlev from @FullContactAPI #NoSQL #BigData (16 Feb)
• Atabey_Kaygun: New post: Information content of n-grams http://t.co/5m8uf34CJR (12 Feb)
• jakevdp: @fonnesbeck We used PyMC extensively in our textbook: http://t.co/Js455BO4dc Code is available here: http://t.co/26ZyJH4myv (12 Feb)
• cboettig: Great overview of date and time tools in #rstats http://t.co/UaExFOsr6T ht @noamross (12 Feb)
• flowingdata: .@civilstat describes his first semester as a stat PhD student http://t.co/RVMog6dao4 (12 Feb)
• genetics_blog: “There is no Such Thing as Biomedical #BigData” http://t.co/AbrDv78dHo provocative post by @vubush (12 Feb)
• zoltanvarju: Free Statistics Text from Computer Science TA http://t.co/vojSGiImZG (11 Feb)
• statsepi: A visual guide to item response theory http://t.co/GYOcUNNYU5 (11 Feb)
• trebor74hr: list of countries in #json - all needed data - name, native name, currency, tld, languages, … very nice - https://t.co/sDy488pRU4 (11 Feb)
• EpiFunky: Simulation-based power analysis for mixed models in lme4 http://t.co/Z4l5iHIYRj (11 Feb)
• HNTweets: A 12" Apple laptop: http://t.co/ZK88ArAzaa Comments: https://t.co/AYeuvLpp9P (11 Feb)
• KennethGeers: FREE #cybersecurity books from #NATO think tank http://t.co/lwDBphiLWh (11 Feb)
• nedgar: .@b0rk’s pandas cookbook / tutorial is really excellent #python #data #analysis https://t.co/PAvkrnoyas (10 Feb)
• wesmckinn: pandas 0.13.1 is out! lots of bug fixes, improved SQL integration, perf enhancements, and more: http://t.co/Ghae6L8IB9 #pydata (9 Feb)
• strataconf: Popular approaches for deploying complex #data projects include scripts, notebooks & workflow tools http://t.co/6uE4mcyWR3 (9 Feb)
• neilfws: Bookmarked: BMJ » Blog Archive Medical research—still a scandal http://t.co/qHBryERLFu (9 Feb)
• ArthurFlam: Some of the most impressive TeX illustrations I’ve seen http://t.co/mlwcPN23hZ via #HN (9 Feb)
• JonesZM: data datum http://t.co/U5RHaHy5eE (9 Feb)
• twiecki: mpld3 – Bridging Matplotlib and the Browser – gets a homepage: http://t.co/QmrtajU69S by @jakevdp (9 Feb)
• kaz_yos: #rstats Using roxygen to create .Rd docs from in-source docs http://t.co/VYhImP6GyB http://t.co/9yQBWtHvEf (9 Feb)
• newsycombinator: The Computational Complexity of Machine Learning https://t.co/QnHcWZhyaN (9 Feb)
• kaz_yos: #rstats Roxygen2 allows documenting functions directly in their source code. This is much better than arcane .Rd http://t.co/2keehJ2aws (9 Feb)
• kaz_yos: #rstats Rd2roxygen converts the old-fashioned .Rd documents and put the roxygen to source files automatically!⁰⁰http://t.co/AgHFkkDH13 (9 Feb)
• StatsBlogs: Bayesian analysis of sensory profiling data, part 2 http://t.co/TebubLdz84 (9 Feb)
• kwbroman: polymode is a convenient emacs mode for things like R+Markdown, and I just got it working for R+Asciidoc https://t.co/XreuCpoW7k (9 Feb)
• octonion: I hadn’t realized Stanford was running its free Introduction to Databases again. Highly recommended. http://t.co/B9zpIhjkDo (9 Feb)
• elsanto_wa: For people that want to use Jekyll and Orgmode together see this tutorial http://t.co/Fu4eEiNpvV #emacs #jekyll #orgmode #github (9 Feb)
• pycoders: epipy - #python tools for epidemiology http://t.co/w0gX6p5CMn (9 Feb)
• drago_carlo: Free online book “Statistical foundations of Machine Learning” http://t.co/suM0y24d9G via @siah #MachineLearning #Statistics #DataAnalysis (8 Feb)
• lucaborger: “Introduction to R” by Germán Rodríguez from Princeton - available on @Rbloggers: http://t.co/B6t0ezQdyN #rstats HT @Protohedgehog (8 Feb)
• fperez_org: The @authorea platform supports IPython Notebooks for interactive data-driven documents: https://t.co/ch6si7cY11 (8 Feb)
• xah_lee: learn Python in a day. Condensed tutorial by example. http://t.co/5YSqWm97zo (8 Feb)
• d3visualization: Running Matlab in an IPython Notebook with Plotly http://t.co/SGVgcPJuVU by @plotlygraphs (8 Feb)
• todd_gureckis: this is interesting http://t.co/ZJgBjlH355 (7 Feb)
• UnixToolTip: Find is a beautiful tool: http://t.co/h3wiN9tQTd (7 Feb)
• bmartinez: All you need to know to start plotting Maps in R (from the workshop I did in the Madrid R meeting) http://t.co/Ua0MbuA9t7 #rstats #dataviz (7 Feb)
• _onionesque: I like Kandinsky on the cover of this new Machine Learning textbook (by Shalev-Shwartz and Ben-David) http://t.co/85Uan9DjU5 :) (7 Feb)
• masemresearch: R - Generalized linear Models - Short Introduction #rstats | Princeton | @scoopit http://t.co/tbmWKeqbbQ (7 Feb)
• JFPuget: Is Python really supplanting R for data work? http://t.co/yCSfSsmpPe via @computerworld (6 Feb)
• HNTweets: Guide to Advanced Programming in C: http://t.co/OpZocEHd8r Comments: https://t.co/fVTnytdEEn (6 Feb)
• siah: Free online book “Statistical foundations of machine learning” http://t.co/G97O0naZPf (6 Feb)
• kevinduh: Deep Learning short course - lecture videos are now on to Youtube for easy viewing: http://t.co/HeeUM2RTGt Hope it’s helpful! (6 Feb)
• freakonometrics: “An Economist’s Guide to Visualizing Data” by Jonathan Schwabish http://t.co/psPJbIClVv HT @TimHarford (5 Feb)
• hspter: This rocks – knitr in a knutshell http://t.co/TH2WPOu4eA by @kwbroman. I make a small cameo :) #rstats (5 Feb)
• Atabey_Kaygun: Statistical Machine Translation http://t.co/VGWOOm2Xmt (5 Feb)
• Atabey_Kaygun: Data on more than 10,000 cancer genomes released by the International Cancer Genome Consortium http://t.co/FamQASDQ2Y (5 Feb)
• michaelwaskom: @fonnesbeck seaborn.boxplot(df.dv, groupby=df.group) (4 Feb)
• Atabey_Kaygun: Data Mining as Exploratory Data Analysis http://t.co/Q9NQx0uglj (4 Feb)
• piccolbo: @_inundata @hadleywickham http://t.co/ZfebNcbrpi (4 Feb)
• shoha99: a gallery of #d3js interactive #datavis http://t.co/oH4KwEF6LA #DataVizQMSS (4 Feb)
• HNTweets: Cactus for Mac: http://t.co/RH5AL2o5Jy Comments: https://t.co/flrIayxlxM (4 Feb)
• statisticsblog: Interactive Location Recommendation using #Tableau and #R. http://t.co/OavWu9igen (4 Feb)
• tslumley: @hadleywickham I’ve become less enthusiastic about microbenchmark after Radford Neal’s post http://t.co/dFxQbbkGaM (4 Feb)
• CompSciFact: Short introduction to topology for computer science grad students http://t.co/VhjVbeR3Ow (4 Feb)
• hadleywickham: my take on #rstats performance: http://t.co/OVsGpeVvm4. As always feedback and pull requests are much appreciated :) (4 Feb)
• mikedewar: The Julia language featured in Wired! http://t.co/GKMXk8XlEg (4 Feb)
• paulblaser: Picking a gui interface for R http://t.co/CgguWxb8O7 via @rbloggers (4 Feb)
• simplystats: .@StatFact The central dogma of statistics (via Josh Akey’s lecture notes http://t.co/wizj1XiaKW) http://t.co/uONAd663bF (4 Feb)
• openscience: Why @PLOSONE is a better way to address reproducibility of biomedical research http://t.co/JkBopOiuiy by @dbasanta #openscience #openaccess (4 Feb)
• RPsychologist: Just updated my Cohen’s d viz with a settings panel, user can now change CER, max ES and step size. http://t.co/Ak19WeI94c (3 Feb)
• trinary: My slides from #d3js last week: http://t.co/t1L0OLRxBy Monte’s slides on IPy+d3: http://t.co/e5ZOmVhRP6 GH link in first slide of each. (3 Feb)
• 2ndquadrant_it: #barman 1.3.0 released! #postgresql #backup #DisasterRecovery #opensource #postgres http://t.co/1HbwYNL8zj http://t.co/AqpuBaoePZ (3 Feb)
• shiffman: In time for class today #natureofcode intro, chapters 1 and 2 ported to p5.js https://t.co/eFOjwC2rAz (3 Feb)
• flavioclesio: Book review: “Doing Data Science” by Rachel Schutt and Cathy O’Neil - http://t.co/yMZkM9B3fh (3 Feb)
• adereth: Where LISP fits (in the theory of computation): http://t.co/XF5XDdsEGt (3 Feb)
• peterrowlett: New blog post: Why do 0! and a^0 equal 1? http://t.co/s2qWMfxUfZ (2 Feb)
• johnros2013: Dendrite: Combining Titan+GraphLab into a powerful suite http://t.co/OOdaNjAkhF (2 Feb)
• blattnerma: Sparse Bayesian Unsupervised Learning #machinelearning #bayesian http://t.co/CKXGk3MIUX (2 Feb)
• HlthAnalysis: Beautiful & informative small multiple #dataviz!! A Stark Gap in Breast Cancer Deaths in the U.S. http://t.co/CLXNmSt2Ph #WordlCancerDay (2 Feb)
• JeromyAnglim: boxplot by group shiny application http://t.co/kwKoWDZKDA #rstats (2 Feb)
• rasbt: R Vs. Python: Putting R and Python to a series of tests to showcase their relative strengths and weaknesses: http://t.co/7G9urWb2Ah (2 Feb)
• jenningsgreg: R vs Python, on Web scraping - http://t.co/OYxUvmeZtk #datascience (2 Feb)
• randal_olson: Great conversation about everyday uses of #Python scripts: http://t.co/CyoiEvqihj /cc @swcarpentry (2 Feb)
• ronert_obst: Parallel Computation for Data Science: with Examples in R and Beyond http://t.co/ID3UQ0Whm3 (1 Feb)
• RPsychologist: New #d3js visualization: Understanding Statistical Power and Significance Testing http://t.co/JOMqN8coE6 (1 Feb)
• Atabey_Kaygun: New post: Sentiment analysis using word distances http://t.co/r0Iuj1orCD (1 Feb)
• laurencstill: Daft: Generate Beautifully Rendered Probabilistic Graphical Models w/ #Python http://t.co/tnRRG4cm95 #dataviz (1 Feb)
• twiecki: Re-Meta-Analysis of Vitamin D effect on all cause mortality using PyMC3 http://t.co/qZrXgMf8J1 (1 Feb)