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I look around and I see myself surrounded by people promising a new dawn of a glorious civilization by machine learning, with perfect data capture, incredible computational and remorseless algorithms crunching to give you a glimpse of the future. Some even say a few billion here and there and you could approach a new singularity in time.
So I wonder, and I wonder, if the human race , particularly the common man, the plebians, ever stand a chance against the machine learning plutocrats.
And I am heartened by that old friend of mankind, the sheer elegant romance of random number creation.And of tools like sarcasm, and implied association using lingustic tools that no database can capture.Of for some more randomness, more romantic entropy, onwards to a brave new utopian internet in an increasingly dystopian world.
Quo Vadis, Brutus? Quo Vadis, indeed!!
Good Artists Borrow, Great Artists Steal-Picasso (allegedly)
Just 1 hour ago, someone wrote this
Just some time back, I wrote this
But as they say–
Karma is a bitch
This is a delicious movie meant to be savored by everyone. It shows how a stood up traditional bride decides to go on her planned honeymoon trip alone. Above all, it shows brown and yellow and white people and black people can have lots of fun together once they shed their stereotypes. Watch it for the lovely colors and nice acting. Great songs and great humor, which you can relate to especially if you are Punjabi /North India.
Go watch and dance with the Queen
There is a dedicated CRAN task view on the various options within the R ecosystem for Clinical Trails
CRAN Task View: Clinical Trial Design, Monitoring, and Analysis http://cran.r-project.org/web/views/ClinicalTrials.html This task view gathers information on specific R packages for design, monitoring and analysis of data from clinical trials. It focuses on including packages for clinical trial design and monitoring in general plus data analysis packages for a specific type of design. Also, it gives a brief introduction to important packages for analyzing clinical trial data . It has the following sub headings.
- Design and Monitoring
- Design and Analysis
- Analysis for Specific Designs
- Analysis in General
and an additional one on PharmaKinetic Data
CRAN Task View: Analysis of Pharmacokinetic Data http://cran.r-project.org/web/views/Pharmacokinetics.html The primary goal of pharmacokinetic (PK) data analysis is to determine the relationship between the dosing regimen and the body’s exposure to the drug as measured by the nonlinear concentration time curve or related summaries (e.g. the area under the curve). Base R contains nls which can be used to calculate nonlinear least-squares estimates of the parameters from a PK model. It returns an object of the class "nls" having methods coef(),formula(), resid(), print(), summary(), AIC(), fitted() and vcov(). Four packages are available in CRAN that directly aid in PK data analysis, including; the packages PK, PKfit, nlmeODE which incorporates nlme and deSolve, and the package PKtools. While PK provides basic pharmacokinetics functions which implement non-compartmental analysis methods, the latter three packages focus on modeling methods.
A relatively new [package] is Greport
http://biostat.mc.vanderbilt.edu/wiki/Main/Greport The greport package contains many functions useful for monitoring and reporting the results of clinical trials and other experiments in which treatments are compared. LaTeX is used to typeset the resulting reports, recommended to be in the context of knitr. The Hmisc and lattice packages are used by greport for high-level graphics.
(NOTE- I wish there was some way to group packages and functions along business domains like finance, telecom, pharma, retail , entertainment) the current system of CRAN views is purely a clustering of packages of similar techniques but not business domains)
Nice tool here courtesy Microsoft
What is Erdos Number
The same principle has been proposed for other eminent people in other fields.
Here are two of them-
you can see how prolific Hadley and Ripley are
A very basic example of why R Documentation is “expert friendly” while SAS language documentation is “user friendly”
From- a Quora thread