## Friday, December 21, 2012

### Cinnarch

Cinnarch is a new Linux distro. It combines the minimalist design of Arch and the cinnamon desktop environment.

## Friday, December 14, 2012

## Wednesday, December 12, 2012

## Friday, December 07, 2012

### GCC for windows

TDM-GCC is good windows port of the GCC tool set. It has been integrated into the windows version of Code::Blocks.

## Tuesday, December 04, 2012

### Knitr problem

I got some weird problem with my R workflow recently. For example, the very simple code chunk below:

--------------------------------------------------

\documentclass[preview]{standalone}

\begin{document}

\begin{figure}

<dev='tikz'>>=

library(ggplot2)

qplot(displ, hwy, data = mpg, colour = factor(cyl))

@

\end{figure}

\end{document}

--------------------------------------------------

would not build correctly. Sometimes it gave error message, sometimes it created empty page, etc. Installing the latest version of knitr from the github repo seemed to solve the problem.

--------------------------------------------------

\documentclass[preview]{

\begin{document}

\begin{figure}

<

library(ggplot2)

qplot(displ, hwy, data = mpg, colour = factor(cyl))

@

\end{figure}

\end{document}

--------------------------------------------------

would not build correctly. Sometimes it gave error message, sometimes it created empty page, etc. Installing the latest version of knitr from the github repo seemed to solve the problem.

## Friday, November 30, 2012

### Using Rcpp with Rstudio

It is quite amazing to see how easy and straightforward it has become to integrate C++ and R, with the help from Rcpp (especially with the new "attribute" function) and Rstudio. More information can be found here.

## Thursday, November 29, 2012

### Generic computers and open source software

The combination of generic hardware and open source software makes sense because of the reasons list here.

## Wednesday, November 28, 2012

### Knitr, Rstudio, and TikZ

I did not realize that this is such a powerful combination until today. It makes it possible to directly work on a Sweave file that has both text and code in the same document quite attractive (as opposed to begin an R program and a LaTeX file, then combine them together in the end).

### Do not use GTK 3 theme wit Mate desktop

I am not sure why, but using GTK 3 theme with Mate desktop significantly slows it down.

## Tuesday, November 20, 2012

### Extra themes for ggplot2

Ggthemes is a new package that provides additional themes for ggplot2. It can even be used to draw a Stata-like graph. I particularly like the Tufte-style, as outlined in

*The Visual Display of Quantitative Information.*## Monday, November 19, 2012

### Mint Debian

I finally got tired of the old Ubuntu 10.04, which has bee sitting in my main computer for over two years. Instead of a more recent version of Ubuntu or Mint, I replaced it with Mint Debian. I then followed the instruction in this post to install the proprietary NVIDIA driver to get the full power of my graphics card. I really like the simple and elegant design of the Mate desktop and hope I don't ever need to re-install system on this machine ever again (because Mint Debian is a rolling distribution).

## Wednesday, November 14, 2012

### Zelig 4 is trouble

Zelig 4 is causing so many problems. I had to uninstall it and went back to the old 3.55.

## Sunday, November 11, 2012

## Tuesday, November 06, 2012

## Sunday, November 04, 2012

## Tuesday, October 23, 2012

## Thursday, October 18, 2012

### Comparison between OpenBUGS, JAGS, and Stan

I have been playing with the examples of

n1 <- 6000 # Number of females

n2 <- 4000 # Number of males

mu1 <- 105 # Population mean of females

mu2 <- 77.5 # Population mean of males

sigma <- 2.75 # Average population SD of both

n <- n1+n2 # Total sample size

y1 <- rnorm(n1, mu1, sigma) # Data for females

y2 <- rnorm(n2, mu2, sigma) # Date for males

y <- c(y1, y2) # Aggregate both data sets

x <- rep(c(0,1), c(n1, n2)) # Indicator for male

The BUGS program looks like this:

model {

# Priors

mu1 ~ dnorm(0,0.001)

mu2 ~ dnorm(0,0.001)

tau1 <- 1 / ( sigma1 * sigma1)

sigma1 ~ dunif(0, 1000) # Note: Large var. = Small precision

tau2 <- 1 / ( sigma2 * sigma2)

sigma2 ~ dunif(0, 1000)

# Likelihood

for (i in 1:n1) {

y1[i] ~ dnorm(mu1, tau1)

}

for (i in 1:n2) {

y2[i] ~ dnorm(mu2, tau2)

}

# Derived quantities

delta <- mu2 - mu1

}

*Introduction to WinBUGS for Ecologists*in the past two days. One thing that concerns me particularly is how well these Bayesian packages handle large data set (i.e., N>10,000), which is the size of the data sets that I work with. So I modified the data generation program provided by the authors and increased the sample sizes:n1 <- 6000 # Number of females

n2 <- 4000 # Number of males

mu1 <- 105 # Population mean of females

mu2 <- 77.5 # Population mean of males

sigma <- 2.75 # Average population SD of both

n <- n1+n2 # Total sample size

y1 <- rnorm(n1, mu1, sigma) # Data for females

y2 <- rnorm(n2, mu2, sigma) # Date for males

y <- c(y1, y2) # Aggregate both data sets

x <- rep(c(0,1), c(n1, n2)) # Indicator for male

The BUGS program looks like this:

model {

# Priors

mu1 ~ dnorm(0,0.001)

mu2 ~ dnorm(0,0.001)

tau1 <- 1 / ( sigma1 * sigma1)

sigma1 ~ dunif(0, 1000) # Note: Large var. = Small precision

tau2 <- 1 / ( sigma2 * sigma2)

sigma2 ~ dunif(0, 1000)

# Likelihood

for (i in 1:n1) {

y1[i] ~ dnorm(mu1, tau1)

}

for (i in 1:n2) {

y2[i] ~ dnorm(mu2, tau2)

}

# Derived quantities

delta <- mu2 - mu1

}

While the Stan looks like this:

data {

int n1;

int n2;

real y1[n1];

real y2[n2];

}

parameters {

real alpha1;

real alpha2;

real sigma1;

real sigma2;

}

model {

y1 ~ normal(alpha1, sigma1);

y2 ~ normal(alpha2, sigma2);

}

generated quantities {

real dif;

dif <- sigma1 - sigma2;

}

For 20,000 iterations, OpenBUGS took 7852.813 seconds, JAGS took 2252.273 seconds, Stan took 60-150 seconds (the Stan team is still trying to iron out some bugs, which may explain the wide range of run time). While the run time of both OpenBUGS and JAGS are sensitive to the sample size, the run time of Stan seems much less sensitive to sample size.

However, if the Stan code is written as this:

model {

for (n in 1:n1)

y1[n] ~ normal(alpha1, sigma1);

for (n in 1:n2)

y2[n] ~ normal(alpha2, sigma2);

}

Then Stan has no apparent performance advantage over JAGS.

However, if the Stan code is written as this:

model {

for (n in 1:n1)

y1[n] ~ normal(alpha1, sigma1);

for (n in 1:n2)

y2[n] ~ normal(alpha2, sigma2);

}

Then Stan has no apparent performance advantage over JAGS.

## Tuesday, October 16, 2012

## Monday, October 15, 2012

### Stan code for my favorite book

I am learning Stan. So I decided to translate the BUGS code for my favorite applied Bayesian book into Stan code. I just did the translation for a couple of chapters and I feel I know Stan much better already.

## Sunday, October 14, 2012

### Insightful analysis of the recent quarrel between China and Japan

I've seen lots of economic, political, and social analysis about it, but this cultural analysis is very interesting and insightful.

## Saturday, October 13, 2012

### From BUGS with BRugs to JAGS with rjags

John Kruschke provided helpful suggestions about how to convert BUGS programs into JAGS. It would be interesting to see some suggestions about how to convert BUGS or JAGS programs into Stan.

## Friday, October 12, 2012

### Stan and INLA

Since both Stan and INLA provide their own implementation of the classical BUGS examples, it is very illustrative and educational to compare the two sets of codes.

For example, the INLA code for the "seeds" example (my of my favorites) is like this:

library(INLA)

data(Seeds)

formula = r ~ x1*x2+f(plate,model="iid")

mod.seeds = inla(formula,data=Seeds,family="binomial",Ntrials=n)

## improved estimation of the hyperparameters

h.seeds = inla.hyperpar(mod.seeds)

while the Stan code is like this:

data {

int I;

int n[I];

int N[I];

real x1[I];

real x2[I];

}

parameters {

real alpha0;

real alpha1;

real alpha12;

real alpha2;

real tau;

real b[I];

}

transformed parameters {

real sigma;

sigma <- 1.0 / sqrt(tau);

}

model {

alpha0 ~ normal(0.0,1.0E3);

alpha1 ~ normal(0.0,1.0E3);

alpha2 ~ normal(0.0,1.0E3);

alpha12 ~ normal(0.0,1.0E3);

tau ~ gamma(1.0E-3,1.0E-3);

b ~ normal(0.0, sigma);

for (i in 1:I) {

n[i] ~ binomial(N[i], inv_logit(alpha0

+ alpha1 * x1[i]

+ alpha2 * x2[i]

+ alpha12 * x1[i] * x2[i]

+ b[i]) );

}

}

INLA has a more R-like syntax and is much more terse, but Stan is much more flexible and can handle very complicated models that INLA may have troubles with.

For example, the INLA code for the "seeds" example (my of my favorites) is like this:

library(INLA)

data(Seeds)

formula = r ~ x1*x2+f(plate,model="iid")

mod.seeds = inla(formula,data=Seeds,family="binomial",Ntrials=n)

## improved estimation of the hyperparameters

h.seeds = inla.hyperpar(mod.seeds)

while the Stan code is like this:

data {

int

int

int

real x1[I];

real x2[I];

}

parameters {

real alpha0;

real alpha1;

real alpha12;

real alpha2;

real

real b[I];

}

transformed parameters {

real

sigma <- 1.0 / sqrt(tau);

}

model {

alpha0 ~ normal(0.0,1.0E3);

alpha1 ~ normal(0.0,1.0E3);

alpha2 ~ normal(0.0,1.0E3);

alpha12 ~ normal(0.0,1.0E3);

tau ~ gamma(1.0E-3,1.0E-3);

b ~ normal(0.0, sigma);

for (i in 1:I) {

n[i] ~ binomial(N[i], inv_logit(alpha0

+ alpha1 * x1[i]

+ alpha2 * x2[i]

+ alpha12 * x1[i] * x2[i]

+ b[i]) );

}

}

INLA has a more R-like syntax and is much more terse, but Stan is much more flexible and can handle very complicated models that INLA may have troubles with.

## Thursday, October 11, 2012

### Some thoughts on Stan

Stan is very promising. The glmmBUGS package should be easily extended to produce Stan code in place of or in addition to BUGS/JAGS code, which will makes it even easier for novice uses to get started.

## Wednesday, October 10, 2012

## Tuesday, October 09, 2012

### Prediction, missing data, etc. in Stan

library(rstan)

N <- 1001

N_miss <- ceiling(N / 10)

N_obs <- N - N_miss

mu <- 3

sigma <- 2

y_obs <- rnorm(N_obs, mu, sigma)

missing_data_code <-

'

data {

int N_obs;

int N_miss;

real y_obs[N_obs];

}

parameters {

real mu;

real sigma;

real y_miss[N_miss];

}

model {

// add prior on mu and sigma here if you want

y_obs ~ normal(mu,sigma);

y_miss ~ normal(mu,sigma);

}

generated quantities {

real y_diff;

y_diff <- y_miss[101] - y_miss[1];

}

'

results <- stan(model_code = missing_data_code,

data = list(N_obs = N_obs, N_miss = N_miss, y_obs = y_obs))

y_diff <- apply(extract(results, c("y_miss[1]", "y_miss[101]")), 1:2, diff)

## Sunday, September 30, 2012

## Friday, September 28, 2012

## Thursday, September 13, 2012

### R2MLwiN package

The new package R2MLwiN package bridges R and MLwinN software for multilevel analysis. From the examples provided, it looks very promising. It will be great if a command line version of MLwiN can be made available under Linux so the package can be useful to everybody. Also, it will be great if a similar package for Mplus can be developed in the future.

There is also a Stata package that connects MLwiN with Stata. A quick glance suggests that the two packages have similar functions.

There is also a Stata package that connects MLwiN with Stata. A quick glance suggests that the two packages have similar functions.

## Friday, September 07, 2012

## Friday, August 31, 2012

### Stan

Stan is a new software package for Bayesian analysis. It also comes with an R interface. According to this benchmark, it may be a viable choice for real world data analysis. I took a similar approach as ADMB by first converting a syntax file into C++ source files and then generating native machine code.

## Monday, August 13, 2012

## Sunday, August 12, 2012

## Monday, August 06, 2012

### CDE becomes free software

According to this source, CDE (common desktop environment) is now free software. Not sure at this moment whether that will make any real difference to our daily computing though.

## Thursday, June 28, 2012

### Chinees GIS resources

Here is a list of useful Chinese GIS resources:

- CHGIS (download and CD)
- China Dimensions
- CloudMade (dowload)
- GRASS-Wiki

## Monday, June 18, 2012

## Friday, June 15, 2012

## Monday, June 11, 2012

### Installed R packages

I did not realize I have such a long list of R packages install (ls /usr/local/lib/R/library >> list.txt):

abind | e1071 | labeling | optimx | sabreR |

ade4 | Ecdat | LaF | optmatch | sandwich |

ade4TkGUI | effects | LaplacesDemon | orthopolynom | scales |

AICcmodavg | ellipse | lars | parallel | segmented |

akima | emdbook | lattice | parser | sem |

Amelia |
Epi | lava | pcaPP | sfsmisc |

anchors | etable | lavaan | permute | shapefiles |

aod | etm | leaps | pgfSweave | simecol |

ape | evaluate | lessR | pixmap | SiZer |

apsrtable | expm | lme4 | pkgmaker | sna |

arm | ff | lmtest | plm | snow |

base | fields | locfit | plyr | snowfall |

bayesDem | filehash | lpSolve | polynom | sos |

bayesLife | flexmix | magic | popbio | sp |

bayesm | FME | mapdata | popdemo | spacetime |

bayesPop | foreach | mapproj | primer | spam |

bayesTFR | forecast | maps | prodlim | SparseM |

BayesX | foreign | maptools |
proto | spatial |

BayesXsrc | formatR | markdown | pscl | SpatialEpi |

bbmle | Formula | MASS | psych | spatstat |

bdsmatrix | forward | Matching | qgraph | spBayes |

bibtex | fracdiff | MatchIt |
quadprog | spdep |

biganalytics | gam | Matrix | quantreg | spgwr |

biglm | gamlss | matrixcalc | R2BayesX |
sphet |

bigmemory | gamlss.data | MatrixModels | R2HTML | splancs |

bigtabulate | gamlss.dist | maxLik | R2jags |
splines |

BiocGenerics | gclus | MBA | R2WinBUGS | splm |

BiocInstaller | gdata | MBESS | RandomFields | stashR |

bit | gee | mclust | randomForest | statmod |

bitops | geepack | MCMCglmm |
RANN | stats |

boot | geoR | MCMCpack |
raster | stats4 |

brew | ggplot2 |
McSpatial | RColorBrewer | stringr |

cacheSweave | gof | mediation | Rcpp |
SuppDists |

cairoDevice | gpclib | memisc | RcppArmadillo | survey |

car | gplots | memoise | rcppbugs | survival |

caTools | graph | MEMSS | RcppDE | svUnit |

cem | graphics | methods | RcppEigen | systemfit |

chron | grDevices | mets | RCurl | tables |

class | grid | mgcv | registry | tabplot |

classInt | gstat | minpack.lm | reldist | tcltk |

cluster | gsubfn | minqa | reshape | tensorA |

coda | gtools | miscTools | reshape2 | testthat |

codetools | gWidgets | mitools | rgam | tikzDevice |

colorspace | gWidgetsRGtk2 | mixAK | rgdal | timereg |

combinat | hexbin | mixtools | rgenoud | tools |

compiler | highlight | mlbench | rgeos | triangle |

corpcor | Hmisc | mlmRev | rgl | truncnorm |

cumSeg | ibdreg | mlogit | RGtk2 | tseries |

datasets | igraph | mnormt | rj | TTR |

data.table | igraph0 | modeltools | rjags | ucminf |

DBI | inline | MPV | rJava | utils |

DCluster | int64 | mstate | rj.gd | vcd |

deldir | iplots | multcomp | Rmixmod | vegan |

DEoptim | IPSUR | MuMIn | rms | VGAM |

depmixS4 |
ipw | munsell | robustbase | visreg |

deSolve | iterators | mvtnorm | rockchalk | waveslim |

devtools | JavaGD | ncdf | RODBC | WhatIf |

dichromat | JGR | nlme | rootSolve | XLConnect |

digest | KernSmooth | nnet | rpart | XLConnectJars |

diptest | knitcitations | npmlreg | rrcov | XML |

doBy | knitr |
numDeriv | RSiteSearch | xtable |

doRNG | Rsolnp | xts | ||

DPpackage |
RSQLite | Zelig |
||

RUnit | zic | |||

Rz | zoo |

## Saturday, June 09, 2012

### Natural selection is still with us

This looks very interesting. I cannot wait to get hold of the paper.

## Saturday, June 02, 2012

### Drawing a map

## Thursday, May 31, 2012

### Mint Debian

After about eight months, my Mint Debian has been broken beyond reparation. So I decided to wipe it out and do a fresh installation. This time I am not pointing directly to the Debian repository but to the Mint repository.

I like Mint Debian better than Mint main edition. I have come to the conclusion that as long as I am not directly pointing to the Debian repository (or at least not taking every single update everyday), the installation should be stable enough as my main workstation OS.

I like Mint Debian better than Mint main edition. I have come to the conclusion that as long as I am not directly pointing to the Debian repository (or at least not taking every single update everyday), the installation should be stable enough as my main workstation OS.

## Tuesday, May 29, 2012

### Free statistics text

OpenIntro.org provides a free statistical textbook, along with data sets and supplementary materials.

Ipsur.org is another one.

Ipsur.org is another one.

## Monday, May 28, 2012

## Saturday, May 26, 2012

## Sunday, May 20, 2012

### BayesX

With the new "R2BayesX" package, now it looks like the BayesX software has been fully integrated into the R environment.

The R2BayesX provides a powerful general purpose package with simple and easy to learn syntax (compared to BUGS or ADMB). Now BayesX is under GPL, it will probably be embraced by the free software community wholeheartedly.

The R2BayesX provides a powerful general purpose package with simple and easy to learn syntax (compared to BUGS or ADMB). Now BayesX is under GPL, it will probably be embraced by the free software community wholeheartedly.

## Saturday, May 19, 2012

## Friday, May 18, 2012

### Multinomial logit model model with random effects

I just found out that the "mlogit" package can estimate multinomial logit model with random effects just like aML and GLLAMM.

## Thursday, May 17, 2012

## Monday, May 14, 2012

### New Rstudio is pretty cool

The new version of Rstudio has option to use "knitr" instead of "sweave", which is really cool!

## Saturday, April 28, 2012

## Tuesday, April 24, 2012

### New version of g++, a good reason to upgrade

My Ubuntu 10.04 runs fine on my workstation except for one thing ... its g++4.4.3 does not have the new c++ language feature which is used in software like CppBugs and its R binding called rcppbugs. The idea behind these two packages are very interesting, which gives me a good reason to upgrade to a new version of the OS (Debian, Mint, or Ubuntu).

## Thursday, April 19, 2012

### Is Markdown/Pandoc any good?

Here is a post about LaTeX vs. Markdown. Looks like it is not easy to translate between the two systems automatically.

## Tuesday, April 17, 2012

## Monday, April 16, 2012

### Cinnamon really works

Mint Linux's Cinnamon works rather well on my old laptop. It takes a bit longer to boot up to the desktop than Xfce, but once you get there, the user experience is rather good. In addition, it is very easy to use themes contributed by others (just put them into ~./theme folder).

## Saturday, April 14, 2012

## Wednesday, April 11, 2012

## Tuesday, April 10, 2012

### New output processing package

The "rockchalk" package looks promising. I hope it can be as general as the "esttab" package for Stata.

### A practical Bayesian book

Introduction to WinBUGS for Ecologists is a good practical Bayesian book. I find the sample code very useful. The simple example of forming predictions (p. 116) is straightforward and can be easily tailored to be used in different research situations.

Doing Bayesian Data Analysis: A Tutorial with R and BUGS is another useful practical Bayesian book. My copy just arrived today. Can't wait to play with some of the examples there.

Doing Bayesian Data Analysis: A Tutorial with R and BUGS is another useful practical Bayesian book. My copy just arrived today. Can't wait to play with some of the examples there.

## Monday, April 09, 2012

## Friday, March 30, 2012

### Sex ratio and malnutrition

## Thursday, March 01, 2012

## Sunday, February 26, 2012

## Monday, February 20, 2012

## Sunday, February 19, 2012

## Saturday, February 18, 2012

### Rstudio font

The development version of Rstudio finally has an option to use customized fonts. This is great.

### Do you see the leash on your neck?

According to this post, both Apple and MS are tightening the leash on general desktop computer users by restricting their freedom of installing and running the software they like, with their shiny new OSs.

Fortunately we still have Linux and BSD.

Fortunately we still have Linux and BSD.

## Friday, February 17, 2012

## Monday, February 13, 2012

## Saturday, February 11, 2012

### The "tables" package

The "tables" package makes Sweave a real option for exploratory data analysis and report generation.

## Wednesday, January 25, 2012

## Monday, January 23, 2012

## Sunday, January 22, 2012

### Some Rcpp benchmarks

I ran the Fibonacci number example from the Rcpp package on a number of computers and operating systems. Here are the results:

test replications elapsed relative user.self sys.self

3 fibRcpp(N) 1 0.17 1.0000 0.17 0.00

1 fibR(N) 1 73.62 433.0588 73.47 0.00

2 fibRC(N) 1 74.27 436.8824 74.20 0.03

D. On the same computer running Revolution R Enterprise 5:

test replications elapsed relative user.self sys.self

2 fibRC(N) 1 72.31 1.000000 72.09 0

1 fibR(N) 1 72.99 1.009404 72.79 0

E. On my third laptop (Core 2 Duo 2.50GHz, 2 GB memory) running Mint Debian (g++ 4.6.2):

Why the faster computer performed worse, on both R and Rcpp versions?

A. On my main computer (Core 2 Extreme 3.06GHz, 8 GB memory) running Ubuntu 10.04 (g++ 4.4.3):

test replications elapsed relative user.self sys.self

3 fibRcpp(N) 1 0.148 1.0000 0.14 0.01

1 fibR(N) 1 87.078 588.3649 87.03 0.04

2 fibRC(N) 1 91.209 616.2770 91.14 0.07

B. Same computer running Windows Vista (g++ 4.5.0):

test replications elapsed relative user.self sys.self

3 fibRcpp(N) 1 0.21 1.0000 0.21 0.00

1 fibR(N) 1 92.08 438.4762 90.47 0.05

2 fibRC(N) 1 94.39 449.4762 93.13 0.03

C. On my second laptop (Core 2 Duo 2.53GHz, 4 GB memory) running Windows 7 (g++ 4.5.0):

test replications elapsed relative user.self sys.self

3 fibRcpp(N) 1 0.21 1.0000 0.21 0.00

1 fibR(N) 1 92.08 438.4762 90.47 0.05

2 fibRC(N) 1 94.39 449.4762 93.13 0.03

C. On my second laptop (Core 2 Duo 2.53GHz, 4 GB memory) running Windows 7 (g++ 4.5.0):

test replications elapsed relative user.self sys.self

3 fibRcpp(N) 1 0.17 1.0000 0.17 0.00

1 fibR(N) 1 73.62 433.0588 73.47 0.00

2 fibRC(N) 1 74.27 436.8824 74.20 0.03

D. On the same computer running Revolution R Enterprise 5:

test replications elapsed relative user.self sys.self

2 fibRC(N) 1 72.31 1.000000 72.09 0

1 fibR(N) 1 72.99 1.009404 72.79 0

E. On my third laptop (Core 2 Duo 2.50GHz, 2 GB memory) running Mint Debian (g++ 4.6.2):

test replications elapsed relative user.self sys.self

3 fibRcpp(N) 1 0.148 1.0000 0.148 0.00

1 fibR(N) 1 65.535 442.8041 65.328 0.200

2 fibRC(N) 1 65.664 443.6757 65.492 0.172

Why the faster computer performed worse, on both R and Rcpp versions?

### Rcpp on windows

I got Rcpp working on my windows machine by installing the Rtools bundle. It is not clearly to me how to get GSL installed so the RcppGSL will also work.

## Friday, January 20, 2012

## Wednesday, January 18, 2012

### Package "rgdal" broke

The new version of "rgdal" package cannot be compiled on my system (both Ubuntu and Debian).

UPDATE: they fixed it by releasing a new version (0.7.8).

UPDATE: they fixed it by releasing a new version (0.7.8).

### NetLogo

The NetLogo developers really want to get things right: today they released the 7th release candidate for the new version (v. 5)!

## Tuesday, January 17, 2012

### R is becoming increasingly popular

According to this, R is the 19th most popular language in the first month of 2012!

## Sunday, January 15, 2012

## Thursday, January 12, 2012

### Visual debugger and the debug mode of the autorun R console

The StatET team kept their promise and delivered the autorun R console with debug mode on. This, combined with the visual debugger, makes the StatET a very appealing cross-platform environment for working with R.

## Sunday, January 08, 2012

### Useful python libraries for social scientists

Here is a list of useful python libraries for social scientists.

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