similar to: Introducing the futile.paradigm, a package for functional dispatching in R

Hello developeRs, Apologies in advance for a rather long email, but to describe the problem, I need to step through many details. I have been working on a new dispatching system (futile.paradigm on CRAN) based on functional programming concepts that is an alternative to S3 and S4 dispatching. I use a declarative syntax using guard statements to control the dispatching between function variants. I

Hi wizards, does somebody know Which programming paradigm is the most used for make R packages ? Thanks in advance. -- personal web site: http://www.geocities.com/ricardo_rios_sv/index.html

Hi wizards, I have a question. Which programming paradigm does R handle? . Iam looking for this information but I didn't found nothing. Thanks in advance. personal web site: http://www.geocities.com/ricardo_rios_sv/index.html -- personal web site: http://www.geocities.com/ricardo_rios_sv/index.html

CRAN (and crantastic) updates this week New packages ------------ * DCGL (1.0) Bao-Hong Liu http://crantastic.org/packages/DCGL Functions for basic differential coexpression analyses: gene filtering, link filtering, DCG (Differentially-Coexpressed Gene) identification and DCL (Differentially-Coexpressed Links) identification.Two algorithms,named DCP and DCe, are provided for

Dear list, I was wondering how to best implement some sort of a "plugin" paradigm using R methods and the dispatcher: Say we have a function/method ('foo') that does something useful, but that should be open for extension in ONE specific area by OTHERS using my package. Of course they could go ahead and write a whole new 'foo' method including the features they'd

Hi all We all know the model-controller-paradigm: I have a model "News" which has a corresponding CRUD-controller "NewsController". But now I''m quite unsure about the following... Guess we have normal visitors that visit our site www.??.com/news and we have administrators that create and modify news items. The admin should see an "Edit" link and a

Hi, Being relatively new to OO programming and not so old on R, I noticed the possibility to do OO programming in R. But it seems there is two "paradigms" S3 that seems the old one but is the one used in the R.oo package and S4 which seems more recent As a starting point, which one is best to use? Is R.oo useful? what are the main difference between the two? Any feedback

mingw, llvm 2.6 (buid with llvm-gcc) Example source code: http://npcontemplation.blogspot.com/2008/06/secret-of-llvm-c-bindings.html I change LLVMCreateJITCompiler(&engine, provider, &error); to LLVMCreateJITCompiler(&engine, provider, 3, &error); $ llvm-gcc `llvm-config --cflags` -c fac.c $ g++ `llvm-config --libs --cflags --ldflags core analysis executionengine jit

I've got a complicated semi-parametric model that I'm fitting with mgcv. I start with a model based on theory. Its got lots of interaction terms. I want to winnow it down: removing each interaction term or un-interacted main effect one by one, checking the AIC, and retaining the model that gives me the lowest AIC. I then want to repeat the procedure on the retained model. Here is

Hi, I am analyzing a data set with greater than 1000 independent cases (collected in an unrestricted manner), where each case has 3 variables associated with it: one, a factor variable with 0/1 levels (called XX), another factor variable with 8 levels (X) and a third response variable with two levels (Y: 0/1). I am trying to see if X1 has an effect on the relationship between X2 and the

Hi, I'm trying to get sparse.model.matrix to retain unused levels. I can't seem to get this working through the most obvious routes such as specifying drop.unused.levels = FALSE in the model.frame or trying to pass all levels in xlev,which is an argument to sparse.model.matrix (see code below). Any help would be gratefully received. Cheers, Jarrod fac<-factor(rep(1:10,10),

Dear all, I'm using the mgcv library by Simon Wood to fit gam models with interactions and I have been reading (and running) the "factor 'by' variable example" given on the gam.models help page (see below, output from the two first models b, and b1). The example explains that both b and b1 fits are similar: "note that the preceding fit (here b) is the same as

Hi, I need to resample rows in a data frame by subsets L3 <- LETTERS[1:3] d <- data.frame(cbind(x=1, y=1:10), fac=sample(L3, 10, repl=TRUE)) x y fac 1 1 1 A 2 1 2 A 3 1 3 A 4 1 4 A 5 1 5 C 6 1 6 C 7 1 7 B 8 1 8 A 9 1 9 C 10 1 10 A I have seen this used to sample rows with replacement d[sample(nrow(d), replace=T), ] x y fac 7 1 7 B 2

[I originally posted this on the R-help mailing list, and it was suggested that R-devel would be a better place to dicuss it.] Running ?table? on a factor with levels containing non-ASCII characters seems to result in extremely bad performance on Windows. Here?s a simple example with benchmark results (I?ve reduced the number of replications to make the function finish within reasonable time):

Hello List Folks. I?m trying to do manova using a function which assembles the response (LHS) and factors (RHS) from different sources. When I do so, the model routines complain that I'm passing a list for the RHS when I don't think I am. Here's a toy example: r <- matrix(rnorm(30), ncol = 3) # toy data td <- list(d = rnorm(10), f1 = sample(c("A",

Hi all, I noticed something strange when I ran aov and anova. vtot=c(7.29917, 7.29917, 7.29917) #identical values fac=as.factor(c(1,1,2)) #group 1 has first two elements, group 2 has the 3rd element When I run: > anova(lm(vtot~fac)) Analysis of Variance Table Response: vtot Df Sum Sq Mean Sq F value Pr(>F) fac 1 1.6818e-30 1.6818e-30 0.3333 0.6667 Residuals 1

Hi I'm doing a glm analysis and I have two doubts (at least :) 1) When I run the function it gives a lot of warnings (see below) what they mean ? (may be I'm ignorant about this analysis ...) glm.poisson<-glm(log(Jkij+1)~fac.ano+fac.tri+fac.icesr+fac.mat+fac.ano:fac.icesr+fac.ano:fac.tri,family=poisson()) warnings() 40: non-integer x = 1.252763 41: non-integer x = 1.864785 42:

Hi, what would be a good way of enhancing the ancova function from the HH package when using a 2 factor ANCOVA? The current behaviour for the "ancova" function from package HH is: ---------------------------------------------- | P1 || P1 || P3 || P4 | | PS | <- the lattice strip ---------------------------------------------- | x|| x ||x ||x | |

Suppose I have a dataframe 'd' defined as L3 <- LETTERS[1:3] d0 <- data.frame(cbind(x = 1, y = 1:10), fac = sample(L3, 10, replace = TRUE)) (d <- d0[d0$fac %in% c('A', 'B'),]) x y fac 2 1 2 B 3 1 3 A 4 1 4 A 5 1 5 A 6 1 6 B 8 1 8 A Even though factor 'fac' in 'd' only has 2 levels, but it seems to bear the birthmark

Dear all, I'm trying to understand how the strucchange package is working and I have been looking at the examples given for the Fstats() function. The first example (Nile), shows one peak in the F-stats and one breakpoint is estimated, that can be plotted using the following code ## Nile data with one breakpoint: the annual flows drop in 1898 ## because the first Ashwan dam was built