Here is the latest bag of tweets^*, which covers June 2015.

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

• robertstats: PROMs in #healtheconomics - slides from @OHENews @RoyalStatSoc https://t.co/bV3flKA2hF (30 Jun)
• revodavid: Big news! R Foundation, Microsoft, RStudio and other industry members form R Consortium to support R project: http://t.co/EWax5V8Kc6 #rstats (30 Jun)
• jonsedar: I’ve started using t-SNE for manifold learning & visualising high-dimensional datasets. Jotted a quick demo at: http://t.co/RKeveaTejG (30 Jun)
• DiegoKuonen: MT @DMR_Rosaria: “What is #KNIME?” blog post by @stricklandjs Nice blog post with all you need to know about KNIME. https://t.co/fnoixrbWs3 (30 Jun)
• YhatHQ: Running R in Parallel (the easy way) | #rstats #datascience | http://t.co/49r71htssh (30 Jun)
• mattdesl: Confused by mathematical notation in graphics papers? I’m writing a cheat-sheet for devs: https://t.co/fXvnmwKRRG http://t.co/M5qLgw76pJ (30 Jun)
• DrBunsen: A super cool interactive viz of Melkman’s Algorithm: http://t.co/vmUjGJPybC (29 Jun)
• michaelwaskom: seaborn 0.6 is out! Take a look at the release notes to find out what’s new: http://t.co/JjvR0Ftbqv http://t.co/DIE0oWRZTC (29 Jun)
• HNTweets: Why Wolfram (Mathematica) did not use Lisp: http://t.co/oXofpDuhI7 Comments: https://t.co/tIbTy22FS1 (29 Jun)
• robertstats: New blog post: Introducing #StataStan. Fit #Bayesian models using @mcmc_stan inside @Stata <https://t.co/4Vzv9ftd2q https://t.co/kKW9VyUzZb (29 Jun)
• opencpu: Slides from today’s R summit about streaming json and #mongodb in R with mongolite: http://t.co/Z1AoCcOeXz (28 Jun)
• KirkDBorne: Instructive books for your #DataScience and #BigData stack: http://t.co/f5RMoRQTDj #abdsc #Rstats #Analytics http://t.co/FFvBpJHBYb (24 Jun)
• freakonometrics: MT @fbahoken “Enseigner le Quanti en SHS” http://t.co/yxaugZYU9X (présentations du séminaire en ligne, passionnant) (23 Jun)
• kwbroman: Playing around with #mongoDB and #D3js. https://t.co/fPOLL9IBgx http://t.co/JwnH0cLoqp (22 Jun)
• ucfagls: Need to read paper in more detail but seems to produce sparser models than mgcv & null-space penalty in their tests http://t.co/6cXQnJvPqO (22 Jun)
• davidjayharris: I’ve set a 200-point bounty on my @StackStats question re: confidence intervals for regularized estimates http://t.co/wPeNSGOyP6 (22 Jun)
• YhatHQ: Document Clustering with #Python (ipynb) | http://t.co/I74YRiI7M9 (22 Jun)
• StatGarrett: New @rstudio cheat sheat today: Interactive Web Apps with Shiny (overhauled)! http://t.co/zlVRHJgLfK #rstats http://t.co/PrkCyKcuim (22 Jun)
• statsepi: Sure http://t.co/tnylJovvXD who cites Allison PD. 2002. Missing Data. Thousand Oaks, CA: Sage -> <http://t.co/YkXchPbfWx @AriBFriedman 1/2 (22 Jun)
• aflyax: Prediction intervals for Random Forests: http://t.co/txPbGla4WS #machinelearning #python #scikitlearn (22 Jun)
• ChrisPolis: New blog post/demo: Building a better table w/ visualization and interaction http://t.co/SVoxSHGQR3 #dataviz http://t.co/d2KDrnHFTw (22 Jun)
• statsepi: Transparent Reporting of a prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement http://t.co/Z9K3OZBxLW (21 Jun)
• JustinWolfers: This is the most amazing illustration of Pythagoras’ Theorem, ever. http://t.co/W0YcLzVPL5 (18 Jun)
• treycausey: Nice survey of many concepts and review of notation | Mathematics for Computer Science http://t.co/wcJcgtxrk2 via @mitocw (16 Jun)
• YhatHQ: The Art of Command Line | jlevy/the-art-of-command-line | http://t.co/LnNHKdTnUP (16 Jun)
• amt_shrma: Tutorial on causal inference and modeling in social networks by @jugander @jure http://t.co/4ULadV81wp #ec2015 (16 Jun)
• kwbroman: Lots of great examples at the website for Antony Unwin’s book, Graphical Data Analysis with R http://t.co/EH4lIwC341 http://t.co/v4iBBuWB5z (16 Jun)
• DrBunsen: The Epic Story of Maximum Likelihood—nice read on the history or ML: http://t.co/XI6xEHZcko (16 Jun)
• YhatHQ: A pandas cookbook - Julia Evans | via jvns.ca | http://t.co/ftj5dgAbzG (15 Jun)
• DrBunsen: Another nice looking programming font, with ligatures! http://t.co/GC8KL7H5P4 (15 Jun)
• TomAugspurger: Pandas 0.16.2 has the new pipe method: http://t.co/E7BUVMXw21. It was a good community effort. (13 Jun)
• DiegoKuonen: RT @calestous: How to spot bad #science (and #logic) #scientificevidence @AnneGlover_EU http://t.co/EkOh5HgaGe HT @ferristician (13 Jun)
• chris_bour: Everything about ML models ensembling is here. With codes. Amazing. http://t.co/Ic8DiZjOb6 (13 Jun)
• JinliangYang: Welcome to a Little Book of R for Bioinformatics! — Bioinformatics 0.1 documentation http://t.co/wKcAABuraq (12 Jun)
• Jowanza: Another cool #julialang post; http://t.co/sE0LHTJgn3 (12 Jun)
• rasbt: PySpark + Scikit-learn = Sparkit-learn. Looks definitely cool and useful! https://t.co/qF1GAtiRIw (12 Jun)
• DiegoKuonen: RT @freakonometrics: “False-Positives, p-Hacking, Statistical Power, and Evidential Value” https://t.co/cWrN0L33g8 http://t.co/f2yIXra57y (11 Jun)
• YhatHQ: Text Visualization Browser - A Visual Survey of Text Visualization Techniques | http://t.co/JcIlwv1ntW (9 Jun)
• hadleywickham: e.g. did you know you can do this? (ggplot(mtcars, aes(mpg, wt)) + geom_point()) %+% transform(mtcars, mpg = -mpg) #rstats (9 Jun)
• jedisct1: iOS 8.3 Mail.app inject kit https://t.co/T0dVXAIJSc (8 Jun)
• b__k: Working with survey data free of errors or missing data? Then somebody cleaned it for you, & it’s worth knowing how. http://t.co/J6qf1qUQ5g (8 Jun)
• DiegoKuonen: RT @syvylyze: Beyond Bar & Line Graphs: Time for a New Paradigm http://t.co/FlakChOlYV #dataviz #visualization http://t.co/WbhMPVc7md (8 Jun)
• zedshaw: All of the code for Learn C The Hard Way will be here https://t.co/QYmozGOQtq and MIT licensed. Repo will have all video lecture slides too. (8 Jun)
• deleeuw_jan: Free at last ! Just publish on RPubs. Latest blurb at <http://t.co/9PQElrV0fjW (7 Jun)
• randal_olson: The remarkable distances you can travel on a European train in less than a day: http://t.co/7zqGN6kstq #dataviz http://t.co/Qv34m1TKJ1 (7 Jun)
• Atabey_Kaygun: Emacs and Scala <http://t.co/D6OqEh7G6jW (6 Jun)
• hadleywickham: @cpsievert there is now: <https://t.co/ouGkYjcwsOW. cc @ucfagls (6 Jun)
• rdpeng: Learning the Art of Data Science from Johns Hopkins University <https://t.co/kgywAMLOqDW #mooc via @classcentral (4 Jun)
• ClausWilke: My first R package on CRAN! cowplot—an add-on to ggplot2 that makes producing compound figures super easy. <http://t.co/nboxbIpQI9W #rstats (4 Jun)
• xgrommx: Hi everyone! I uploaded the pdf version of rx-book <http://t.co/KDIHmddG8EW. Thanks @ReactiveX @mattpodwysocki @GitBookIO. This isn’t final (4 Jun)
• treycausey: Prediction intervals for random forests <http://t.co/yr93fBQZKAW (4 Jun)
• YhatHQ: Data Mining and Statistics: What’s the Connection? (pdf) | http://t.co/2L4cTUHSII (4 Jun)
• TwitterOSS: .@davelester congrats and thanks for all you do in making the open source community a better place! http://t.co/lZr6CPjHiM (3 Jun)
• ucfagls: My aversion to pipes in #rstats http://t.co/ddIFmQs31a (3 Jun)
• rasbt: “A tale of three different Data Scientist candidates” – A really interesting and entertaining story http://t.co/6yw2YtapoK (3 Jun)
• DrBunsen: Not sure I could use a Mac without OS X for Hackers. So. Great. https://t.co/b6NqHVj5H5 (2 Jun)
• msgbi: I have 227 browser tabs open, and my computer runs fine. Here’s my secret. http://t.co/H9RwWf0ttB #computer394 (2 Jun)
• juantomas: . @pacoid Databricks Launches MOOC: Data Science on Spark https://t.co/oengdnATT6 (2 Jun)
• KirkDBorne: Mega collection of #DataScience and #BigData #Analytics terminology, +more http://t.co/n8UHHVe3OX #abdsc http://t.co/RCxCyB80GM (1 Jun)