Who made Libre Office

From

 

http://www.libreoffice.org/about-us/credits/

 

Credits

513 individuals contributed to OpenOffice.org (and whose contributions were imported into LibreOffice) or LibreOffice until 2011-11-11 09:02:38.

Developers committing code since 2010-09-28

Ruediger Timm
Commits: 89832
Joined: 2000-10-10
Kurt Zenker
Commits: 32763
Joined: 2000-09-25
Oliver Bolte
Commits: 31795
Joined: 2000-09-19
Vladimir Glazunov
Commits: 30289
Joined: 2000-12-04
Jens-Heiner Rechtien [hr]
Commits: 29314
Joined: 2000-09-18
Ivo Hinkelmann
Commits: 10228
Joined: 2002-09-09
Caolán McNamara
Commits: 5952
Joined: 2000-10-10
Frank Schoenheit [fs]
Commits: 5019
Joined: 2000-09-19
Hans-Joachim Lankenau
Commits: 3077
Joined: 2000-09-19
Ocke Janssen [oj]
Commits: 2861
Joined: 2000-09-20
Mathias Bauer
Commits: 2606
Joined: 2000-09-20
Oliver Specht
Commits: 2458
Joined: 2000-09-21
Philipp Lohmann [pl]
Commits: 2132
Joined: 2000-09-21
Tor Lillqvist
Commits: 2035
Joined: 2010-03-23
Stephan Bergmann
Commits: 1993
Joined: 2000-10-04
Christian Lippka ORACLE
Commits: 1811
Joined: 2000-09-25

We do not distinguish between commits that were imported from the OOo code base and those that went directly into the LibreOffice code base as:
a) it is technically not possible to distinguish between commits that go directly into the LibreOffice code base and commits that were merged in from the OpenOffice.org code base, and
b) contributers to the OOo code base should also be credited for the excellent work they do.

Do note that LibreOffice is divided into 20 git repositories. Pushing a change into all repositories will be counted as 20 commits as there is no way to distinguish this from 20 separate commits.

Total contributions to the TDF Wiki

1223 individuals contributed:

Quantitative Modeling for Arbitrage Positions in Ad KeyWords Internet Marketing

Assume you treat an ad keyword as an equity stock. There are slight differences in the cost for advertising for that keyword across various locations (Zurich vs Delhi) and various channels (Facebook vs Google) . You get revenue if your website ranks naturally in organic search for the keyword, and you have to pay costs for getting traffic to your website for that keyword.
An arbitrage position is defined as a riskless profit when cost of keyword is less than revenue from keyword. We take examples of Adsense  and Adwords primarily.
There are primarily two types of economic curves on the foundation of which commerce of the  internet  resides-
1) Cost Curve- Cost of Advertising to drive traffic into the website  (Google Adwords, Twitter Ads, Facebook , LinkedIn ads)
2) Revenue Curve – Revenue from ads clicked by the incoming traffic on website (like Adsense, LinkAds, Banner Ads, Ad Sharing Programs , In Game Ads)
The cost and revenue curves are primarily dependent on two things
1) Type of KeyWord-Also subdependent on
a) Location of Prospective Customer, and
b) Net Present Value of Good and Service to be eventually purchased
For example , keyword for targeting sales of enterprise “business intelligence software” should ideally be costing say X times as much as keywords for “flower shop for birthdays” where X is the multiple of the expected payoffs from sales of business intelligence software divided by expected payoff from sales of flowers (say in Location, Daytona Beach ,Florida or Austin, Texas)
2) Traffic Volume – Also sub-dependent on Time Series and
a) Seasonality -Annual Shoppping Cycle
b) Cyclicality– Macro economic shifts in time series
The cost and revenue curves are not linear and ideally should be continuous in a definitive exponential or polynomial manner, but in actual reality they may have sharp inflections , due to location, time, as well as web traffic volume thresholds
Type of Keyword – For example ,keywords for targeting sales for Eminem Albums may shoot up in a non linear manner after the musician dies.
The third and not so publicly known component of both the cost and revenue curves is factoring in internet industry dynamics , including relative market share of internet advertising platforms, as well as percentage splits between content creator and ad providing platforms.
For example, based on internet advertising spend, people belive that the internet advertising is currently heading for a duo-poly with Google and Facebook are the top two players, while Microsoft/Skype/Yahoo and LinkedIn/Twitter offer niche options, but primarily depend on price setting from Google/Bing/Facebook.
It is difficut to quantify  the elasticity and efficiency of market curves as most literature and research on this is by in-house corporate teams , or advisors or mentors or consultants to the primary leaders in a kind of incesteous fraternal hold on public academic research on this.
It is recommended that-
1) a balance be found in the need for corporate secrecy to protest shareholder value /stakeholder value maximization versus the need for data liberation for innovation and grow the internet ad pie faster-
2) Cost and Revenue Curves between different keywords, time,location, service providers, be studied by quants for hedging inetrent ad inventory or /and choose arbitrage positions This kind of analysis is done for groups of stocks and commodities in the financial world, but as commerce grows on the internet this may need more specific and independent quants.
3) attention be made to how cost and revenue curves mature as per level of sophistication of underlying economy like Brazil, Russia, China, Korea, US, Sweden may be in different stages of internet ad market evolution.
For example-
A study in cost and revenue curves for certain keywords across domains across various ad providers across various locations from 2003-2008 can help academia and research (much more than top ten lists of popular terms like non quantitative reports) as well as ensure that current algorithmic wightings are not inadvertently given away.
Part 2- of this series will explore the ways to create third party re-sellers of keywords and measuring impacts of search and ad engine optimization based on keywords.

LibreOffice – Extensions and Templates

Just an announcement from The Document Foundation (which has notable supporters including Google etc at http://www.documentfoundation.org/supporters/)

With both Google Docs and Libre Office – it seems like a flank attack on Office productivity software (from the cloud and from the PC/tablet ground)- however Microsoft’s Sharepoint is much better in collobration compared to the Google Docs and it has huge number of templates (more than the 38 extensions and 13 templates right now at the links below (just like WordPress has huge number of themes compared to Blogger)

Anyways, check out- it is an interesting start

http://extensions.libreoffice.org/

Extension Releases 

Extensions for all program modules
Gallery Contents for all program modules
Language Tools for all program modules
Dictionaries of different languages for all program modules
Writer-Extensions
Calc-Extensions
Impress-Extensions
Draw-Extensions
Base-Extensions
Math-Extensions 

….

and http://templates.libreoffice.org/

Template Releases

Accounting -Templates
Agenda-Templates
Arts-Templates
Book-Templates
Brochure/Pamphlet-Templates
Budget-Templates
Business-Templates
Business POS-Templates
Business Shipping-Templates
Calendar-Templates
Card-Templates
Curriculum/Resume-Templates
CD/DVD-Templates
Certificate-Templates
Checkbook-Templates
Christmas-Templates
Computer-Templates
Conference-Templates
E-book-Templates
Education-Templates
Academia-Templates
Elementary/Secondary School-Templates
Envelope-Templates
Fax-Templates
Genealogy-Templates
Grocery-Templates
Invoice-Templates
Labels-Templates
Letter-Templates
Magazine-Templates
Media-Templates
Memo-Templates
Music-Templates
Newsletter-Templates
Notes-Templates
Paper-Templates
Presentation-Templates
Recipe-Templates
Science-Templates
Sports-Templates
Timeline-Templates
Timesheet-Templates
Trades-Templates
To Do List-Templates
Writer-Templates

 

Building a Regression Model in R – Use #Rstats

One of the most commonly used uses of Statistical Software is building models, and that too logistic regression models for propensity in marketing of goods and services.

 

If building a model is what you do-here is a brief easy essay on  how to build a model in R.

1) Packages to be used-

For smaller datasets

use these

  1. CAR Package http://cran.r-project.org/web/packages/car/index.html
  2. GVLMA Package http://cran.r-project.org/web/packages/gvlma/index.html
  3. ROCR Package http://rocr.bioinf.mpi-sb.mpg.de/
  4. Relaimpo Package
  5. DAAG package
  6. MASS package
  7. Bootstrap package
  8. Leaps package

Also see

http://cran.r-project.org/web/packages/rms/index.html or RMS package

rms works with almost any regression model, but it was especially written to work with binary or ordinal logistic regression, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression.

For bigger datasets also see Biglm http://cran.r-project.org/web/packages/biglm/index.html and RevoScaleR packages.

http://www.revolutionanalytics.com/products/enterprise-big-data.php

2) Syntax

  1. outp=lm(y~x1+x2+xn,data=dataset) Model Eq
  2. summary(outp) Model Summary
  3. par(mfrow=c(2,2)) + plot(outp) Model Graphs
  4. vif(outp) MultiCollinearity
  5. gvlma(outp) Heteroscedasticity using GVLMA package
  6. outlierTest (outp) for Outliers
  7. predicted(outp) Scoring dataset with scores
  8. anova(outp)
  9. > predict(lm.result,data.frame(conc = newconc), level = 0.9, interval = “confidence”)

 

For a Reference Card -Cheat Sheet see

http://cran.r-project.org/doc/contrib/Ricci-refcard-regression.pdf

3) Also read-

http://cran.r-project.org/web/views/Econometrics.html

http://cran.r-project.org/web/views/Robust.html

 

Featured: PAW & TAW NYC Hotel Reservations Due This Week

Message from PAWCON-

Space is filling up fast at the Hilton New York, host hotel for Predictive Analytics World and Text Analytics World, next month in New York City. Take advantage of the special room rate negotiated for attendees prior to Friday, September 23rd.

Space is limited so be sure to book your room before it’s too late.

You can reserve your room today by calling             212-586-7000       and reference Data Driven Business Week or online at:
http://www.hilton.com/en/hi/groups/personalized/N/NYCNHHH-RMSP-20111015/index.jhtml?WT.mc_id=POG#reservation

MORE INFORMATION:

PAW: http://www.pawcon.com/nyc
PAW REGISTRATION: http://www.pawcon.com/newyork/register.php

TAW: http://www.tawgo.com/nyc
TAW REGISTRATION: http://www.tawgo.com/newyork/2011/registration

View the PAW overview video: www.pawcon.com/newyork/2011/video_about_predictive_analytics_world.php 

Google Docs Templates

Google Docs has lots of templates but the funny part is they are not well integrated with the individual components, instead you almost have to go to the templates directory first and then to a particular class of document (like presentation)

Within Google Docs presentation, there is no way to go to templates library at https://docs.google.com/templates pictured above

and thats all it shows.

Instead you need to go to the Google Docs homepage and then choose templates. This is slightly opposite to the way people use Office software- you generally decide to use a software and then use a template. Not with Google Docs though- you need to choose template first using either of three methods-