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

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

  • SublimeTxtTips: RT @piercarlo_s #Emacs dired plugin for #sublimetext? Awesome! http://t.co/aW0m6HLVeA (25 May)
  • statslabdublin: 10 things statistics taught us about big data analysis by Jeff Leek | Simply Statistics http://t.co/YSWFiGYt3B (24 May)
  • rasbt: Serving static content (and comments!) in #Python with Pelican https://t.co/TNtp6S2DmV (24 May)
  • DatabaseFriends: Table partitioning in #PostgreSQL http://t.co/nodhzPsRK6 (24 May)
  • ProjetBANO: Premier jeu de données complet publié sur @datagouvfr https://t.co/l44xxL0zSI vos clics sur “Utile” sont bienvenus ! http://t.co/FhMzpc5Nmp (24 May)
  • RenLNS: Great advice about #Git on how to undo a variety of actions: http://t.co/LZpzLiDppJ (24 May)
  • CatchTheBaby: “Finding the Missing Link for Big Biomedical Data” @JAMA_current Map of Biomedical Data http://t.co/V6I7kxglAt http://t.co/8WZjnYSiZI (24 May)
  • Atabey_Kaygun: FACTORIE: a toolkit for probabilistic modeling in #Scala http://t.co/7EKCmG8dTc (23 May)
  • Atabey_Kaygun: Probabilistic Models of Cognition https://t.co/3dsgDG8dMN (23 May)
  • DevilleSy: Also applies to many science papers I guess. RT @HoeflerCo: Right on. http://t.co/UOeE3vv7k3 (23 May)
  • kdnuggets: The Origins of Statistical Computing - http://t.co/LFajOUI3kd (23 May)
  • fonnesbeck: Bayesian model inference and parameter estimation for biological models http://t.co/hjUui72qnj (22 May)
  • joe_pickrell: Moving my mini-tutorial on tools in population genetics to GitHub. Will attempt to teach from this next week. https://t.co/0aaiJI9ouG (22 May)
  • Atabey_Kaygun: Detecting Significant Trends in Text http://t.co/pFyJR92AhC (22 May)
  • rasbt: Took me quite some time to figure out the bash syntax to prepend Python shebangs to .py files…if you ever need it: https://t.co/RF5URal21Q (22 May)
  • dloss: With inline images in iTerm2 and Vim-style key-bindings in #IPython Notebook, I imagine an enhanced IPython console. http://t.co/T1x1kPprEg (22 May)
  • treycausey: cc: @trent_hauck @fonnesbeck MT @StatsbyLopez: PyMC: if anyone could help, your reward will be something spectacular. http://t.co/LvD1GrouqL (22 May)

  • zentree: ‘#Rstats scripts’ on running R code in batch mode. http://t.co/UeY8sCpmm3 (21 May)

  • JulianHi: Machine Learning Tutorial: High Performance Text Processing http://t.co/Ya9XnDD5rw #machinelearning #nlp http://t.co/0dp6XI6qaI (20 May)

  • wesmckinn: New @datapad website for launch: http://t.co/Ww73UQQigs. We’re expanding access to the product as quickly as we can. (20 May)

  • rasbt: “Easy Python Setup for a Mac” - a nice guide by @ag_shen about how she keeps her software organized via homebrew https://t.co/9JqVQx2TjY (20 May)

  • mikeolson: Two excellent machine learning posts, via @sgourley: http://t.co/hEQ0zfPEy9, http://t.co/2fkf3TPL2G (20 May)

  • YhatHQ: Developing Data Products - new Coursera class w/ Yhat! | http://t.co/uenWRsfZ3O (20 May)

  • hadleywickham: New version of roxygen2 now available: http://t.co/OodR4rrWvy. Safer, better errors, and six vignettes tell you how to use it #rstats (20 May)

  • analyticbridge: Could SciDB use R as a wrapper?: http://t.co/X1PRB0TZRb (20 May)

  • kaz_yos: Fixing sequential indentation with #ggplot2 #dplyr in #ess for #rstats https://t.co/psjpjCRFm3 http://t.co/blqp5siOi5 (20 May)

  • robjhyndman: To explain or predict? http://t.co/wmxYx8ufUy (19 May)

  • pushthings4ward: [PDF] An Economist’s Guide to Visualizing Data http://t.co/BrjY91WRM4 | #visulization #dataviz #ddj #journalism (19 May)

  • planetclojure: Clojure Example : Guest Book Application http://t.co/hd2jXTNGOI (18 May)

  • Atabey_Kaygun: (Ab)Using Language Features: The Common Lisp Condition System http://t.co/6hkoDFXKsX (18 May)

  • foil: I spent last year building a collaborative analytics app that integrates #IPython and Google Docs. More here: http://t.co/OhTSF2sEbe (18 May)

  • ibdknox: Awesome blog post by @jamiiecb “Pain we forgot”: http://t.co/mz6gcOO4AQ (18 May)

  • SebastienBubeck: First draft of “Theory of Convex Optimization for Machine Learning”: http://t.co/SmVjJVm8sO (17 May)

  • winston_chang: Some performance tests of #rstats ref classes, compared to alternatives that are made using envirs: http://t.co/Ez1VV6KodW @hadleywickham (17 May)

  • RenLNS: Some of my favorite #Emacs packages: http://t.co/zXz8EfePag (17 May)

  • Atabey_Kaygun: kiama: A #scala library for language processing https://t.co/AMM476GJzH (17 May)

  • zetieum: To read: How evidence-based medicine is failing due to biased trials and selective publication http://t.co/YqWl7lmmJH RT @ivanoransky (17 May)

  • ialuronico: I really like @nature’s 3 pages papers: http://t.co/f0q3bGwcSE Multiple testing correction (16 May)

  • DataTau: Neural networks and a dive into Julia: http://t.co/57preevaIO (16 May)

  • cboettig: fascinated by the collaborative lesson design & iteration of @swcarpentry ’s R courses (e.g. https://t.co/nCZZ3mvD2i ) nice work all! (16 May)

  • statalgo: “The Unreasonable Effectiveness of Data” (2009) http://t.co/mA5TIoCrlD “We should stop [seeking] elegant theories, and embrace complexity” (16 May)

  • moorejh: Probabilistic drug connectivity mapping http://t.co/TWY1hqMSFj #genomics #pharmacology #bioinformatics http://t.co/dHE2WPx2iu (16 May)

  • planetclojure: Clojure Cookbook http://t.co/bJnP6rTizg (16 May)

  • kwbroman: hipsteR: re-educating people who learned #rstats before it was cool. http://t.co/IEvR8jD4tV cc/@tslumley (16 May)

  • alignedleft: “5 Reasons to Learn D3,” feat. work by @mbostock @shancarter @JanWillemTulp @Elijah_Meeks @audenfan @GlobPeaceIndex http://t.co/9H9YcpIiUr (15 May)

  • YhatHQ: Awesome post by Bugra Akyildiz!! Recap/highlights from talks && tutorials @ PyData Silicon Valley 2014 | http://t.co/5BHCKdXsrA @bugraa (14 May)

  • JulianHi: My new tutorial: Cluster your #Twitter data with R and k-means http://t.co/reimisKMwi #machinelearning #rstats http://t.co/DVtyy5WF1Z (14 May)

  • peterneubauer: Two top notch project combined. The @techcrunch Crunchbase 2.0 http://t.co/9caWRee4lW, powered by @neo4j. #in http://t.co/qmFydMH2LT (14 May)

  • berndweiss: Using #Emacs, #Orgmode and #rstats for Research Writing in Social Sciences https://t.co/m8Hk7xE42Y (13 May)

  • statwonk: Nice! The dplyr package in R makes it a dream to use R as an ETL tool! http://t.co/YhJZ25XRpI Bravo, @hadleywickham @romain_francois (13 May)

  • Atabey_Kaygun: GNU Make 4.0 released, including support for plugins (such as #scheme code in Makefiles) http://t.co/RNyh9ixXua (13 May)

  • alexott_en: Incanter + Gorilla REPL integration: http://t.co/OQoMwSJSfp - it’s so nice to see it… (13 May)

  • tristanzajonc: Best book on causal inference: Imbens & Rubin’s “Causal Inference for Statistics, Social, and Biomedical Sciences” http://t.co/JBcrEGgwHM (13 May)

  • worldofpiggy: An R Package that Automatically Collects and Archives Details for #Reproducible #Computing http://t.co/OSlg0HiZPY (12 May)

  • arnicas: My new post about data story characters (and authorship) in vis: http://t.co/GCStbregtO (11 May)

  • benhamner: 2014: “Python is steadily eating other languages’ lunch” http://t.co/RFH9Cx1vru 2016: Julia is steadily eating Python’s lunch (11 May)

  • dloss: Good #Pandas cheatsheets and cookbooks (compute, merge, select, sort, dates, SQL-like, Excel-like). #pydata #IPython https://t.co/6aUDY1Pg2j (11 May)

  • vallens: bashplotlib is like, my favourite thing right now. http://t.co/2fa6mPJjwv (11 May)

  • CompSciFact: Back to the future of databases http://t.co/2RxQgRUtfK via @mqsiuser (11 May)

  • khinsen: New blog post: Exploring Racket @racketlang http://t.co/wvYyE4lDw1 (10 May)

  • kdnuggets: Data Mining for Statisticians http://t.co/9JXCfoK3fs (10 May)

  • teropa: Truth #scotlandjs http://t.co/LiWeZ5uwQ7 (10 May)

  • kdnuggets: Data Mining for Statisticians http://t.co/9JXCfoK3fs (9 May)

  • ucfagls: “Modelling seasonal data with GAMs” new post in my (very) irregular series on GAMs & modelling time series in #rstats http://t.co/Az11srzByT (9 May)

  • MongoDB: Introducing mtools: Diagnostic tools for MongoDB http://t.co/P80557Ucdt by @tomonezero (9 May)

  • OlivierVerdier: Great video tutorials by @andrejbauer on how mathematicians can use the Coq proof assistant http://t.co/Pj7dO4Sj5Q (9 May)

  • SublimeTxtTips: RT @ryentzer: Setting up Sublime Text for Python development [via @dbader_org] http://t.co/k8gO8ilK03 //Found this useful for #python (9 May)

  • jakevdp: “Why Python is Slow” + hacking Python with Python: http://t.co/8AUSuUcpfJ (9 May)

  • scottsburns: When you begin to have related #IPython notebooks (and you will) check out http://t.co/53RQRrDCPi for how to share code between them. (9 May)

  • morganherlocker: geocolor.io: a web app for creating and sharing choropleths with geojson http://t.co/XipWm7P7zJ (9 May)

  • rasbt: That’s what I call a “Python Cheat Sheet” http://t.co/eqkRLBhFAY (9 May)

  • ampp3d: Great graphic showing when antibiotics are discovered http://t.co/tXwW4rV2OY & when resistant strains are identified http://t.co/zjT2lduEWH (9 May)

  • carlcarrie: Graphical visualization of tweets https://t.co/dsZdEv0XMw (9 May)

  • DiegoKuonen: “Lack of expertise in statistics has led to fundamental errors!” http://t.co/2u8NjdqCCe #BigData #Statistics #DataScience #Analytics (9 May)

  • Atabey_Kaygun: Why Atom Can’t Replace Vim http://t.co/zEn10i8oNU via @Prismatic (9 May)

  • RPubsRecent: Literate Programming with R and BigQuery http://t.co/f86sEidIWa (9 May)

  • johnmyleswhite: “Popper, Falsification and the VC-Dimension” by Corfield, Schoelkopf and Vapnik: http://t.co/CTToq8spw4 (via @giures) (8 May)

  • ucfagls: #julialang one “contender” for FORTRAN’s scientific computing crown, on Ars Technica http://t.co/f0rxVNgb7N (haskell, clojure others) (8 May)

  • walkingrandomly: Can you tell a Computer Scientist from the way they write loops? Part 2 http://t.co/OKx2pZIS3D (8 May)

  • Stata: Stata Blog: Using resampling methods to detect influential points http://t.co/KAWohSPnNT (8 May)

  • arun_sriniv: Still trying to figure this nice Q on SO: http://t.co/1K7SYdos5W #rstats Nice follow up from Martin Morgan as well. (8 May)

  • copheehaus: [Blog] “Fifteen ideas about data validation (and peer review)” http://t.co/OKVJW53tYQ (8 May)

  • statsepi: Invited commentary: composite outcomes as an attempt to escape from selection bias and related paradoxes http://t.co/2jLFwPfKj3 AJE (8 May)

  • drewconway: @mikedewar you should totally check out @mikiobraun’s startup: streamdrill https://t.co/8hzehhqbTL (8 May)

  • glouppe: @mblondel_ml This? https://t.co/YLrgmvcSEz :) (8 May)

  • nicebread303: Speed up R on Mac OS by factor 4 (at least benchmark): Only a two-liner in the Terminal! https://t.co/V2nDDImwd4 #rstats (8 May)

  • mdallastella: Jekyll turns 2.0.0 http://t.co/5z8EPMkwFO (8 May)

  • neuro_bit: Reducing Huntingtin in both striatum and cortex has better effects than in either structure alone, Nature Medicine http://t.co/HFb0rhzl2M (8 May)

  • abisen: Twitter data easiest to capture & analyze. Start capturing & charting insights http://t.co/YILHhIIT71 #PyData #pygal (8 May)

  • josephmisiti: Postgres Datatypes – The ones you’re not using. http://t.co/rqsnBpoj99 (8 May)

  • badnetworker: Your ocassional reminder that Francis Fukuyama (yes, that Francis Fukuyama) has a GitHub account: https://t.co/utixPrNcYQ (8 May)

  • jiffyclub: More examples of patsy slowness from @fscottfoti: http://t.co/XSmspI51uL (7 May)

  • gd047: New Shiny article: Style your apps with CSS http://t.co/Iq6NSOgVhz via @wordpressdotcom (7 May)

  • hadleywickham: Have you ever quoted Knuth on premature optimisation? If so read the whole paper http://t.co/afIAfMRrOS. It’s a classic & still relevant (7 May)

  • mbostock: Let’s Make a Bubble Map - thematic mapping with #d3js and #topojson http://t.co/Z55fQ4XZHh http://t.co/nFIAj8TdT3 (7 May)

  • freakonometrics: “A Very Short History Of Data Science” http://t.co/NWZC8JyeF6 and “A Very Short History Of Big Data” http://t.co/iWAnLOwhJh (6 May)

  • HarlanH: The importance of uncertainty - The Berkeley Science Review http://t.co/msrok1m8pw < on communicating w/ error bars (6 May)

  • b__k: On testing statistical software. Probably the more interesting part of the series. http://t.co/rxgwQ1eqsu #selfpromotion (6 May)

  • mikaelhuss: Nice presentation of interactive graphics in R (rCharts, ggvis, Shiny, rMaps, googleVis, qtlcharts) http://t.co/He3AsHmBBg (5 May)

  • bearloga: This comment thread on p-values & conditional probability may actually be my favorite comment thread on the Internet: http://t.co/QWR6evSakg (5 May)

  • jebyrnes: Great comparison of meta-analysis w/ weighted linear or mixed model analyses using #metafor and #rstats http://t.co/qvBzzRJkXG (5 May)

  • trebor74hr: #Python Central | Python Programming Examples, Tutorials and Recipes http://t.co/pz0yvGiUdg (3 May)

  • trebor74hr: thomasballinger/pythonquiz · GitHub https://t.co/2R9468daay (3 May)

  • kumarshantanu: A #Clojure MOOC from University of Helsinki: http://t.co/rQggrMXy2g /via @colinfleming (2 May)