search for: findfuns

Dear list, I have been using "findVar" (defined in src/main/envir.c) happily and would like to use "findFun". However I have trouble when the name searched cannot be found: while "findVar" returns R_UnboundValue, "findFun" does not (the 4 last lines of "findFun" are copied below). error(_("could not find function

I was debugging a problem reported to me regarding PL/R, and found that I can duplicate it using only R sources. It might be characterized as possibly a misuse of the findFun() function, but I leave that for the R devel experts to decide. The below results are all with R-2.5.1 (I can't seem to download r-patched at the moment, but didn't see anything in the 2.5.1-patched release

Dear all, when working on a project with embedded R, I found out that R-2.0.0 causes a problem where older versions worked OK. Rf_findVar(...) causes the following error when used to find a generic function (such as print): fun = Rf_findVar(...); R_tryEval(fun, ...); Error in function (object, ...) : Invalid generic function in usemethod Alternatively, using Rf_findFun(...) seems OK in this

Hi all, I have a problem of Rf_initEmbeddedR function. I've tried with: try { int Argc1 = 1; char *Argv1[] = {"Rtest_1"}; int Argc2 = 1; char *Argv2[] = {"Rtest_2"}; // Init R(first) Rf_initEmbeddedR(Argc1, Argv1); // R package load SEXP e = R_NilValue; SEXP r = R_NilValue; PROTECT(e =

Hi, Is the below -- setSeed -- an okay way to set the seed in standalone applications making use of Rlib? It seems to work as expected. Is there a better way to do it? (I'm also looking at do_setseed but am unsure what to supply as op. findFun("set.seed", R_GlobalEnv)?) Thanks much. -- David Faden, dfaden at iastate.edu AIM: pitulx -- #include <assert.h> #include

Dear List, I'm building an R syntax highlighting file for GeSHi [*] for a website I am currently putting together. The syntax file needs a list of keywords to highlight. How can I generate a list of all the functions in a base R installation? Ideally the list would be formatted like this: "'fun1', 'fun2', 'fun3'" when printed to the screen so I can copy

hello,Everyone! I am a freshman to use R. Can anybody tell me where has scor function which achieves the skipped correlation? Thank you very much! Bset wishes! Jiaolong Qin [[alternative HTML version deleted]]

Simon, Ah, thank you! quiet right. For anyone searching for this in the future, I changed my init fuction to: ---------- SNIP ---------------- void init_r() { SEXP aperm_function; /* this is our version of Rf_initEmbeddedR where we disable stack checking */ const char *init_argv[] = {"MyFront", "--vanilla", "--slave"}; Rf_initialize_R(sizeof

I have a pdf file that I would like to parse into R: http://www.williams.edu/Registrar/geninfo/faculty.pdf For now, I open the file in Acrobat by hand, then save it "as text" and then use readLines(). That works fine but a) I am concerned that some information may be lost and b) I may be doing this a lot, so I would rather have R grab the information from the pdf file directly. So: is

So I'm working on a custom front end to R, in one mode of the front end I dynamically load libR.so into a child worker thread. I'm very careful to make sure it is loaded by a single thread and loaded only once, but since it is a child thread it violates assumptions made by the stack size checking inside of R and I get innumerable errors along the lines of Error: C stack usage

I have an object with an attribute "tframe" for which there is a generic method print.tframe and also a method print.tframe.default defined in a library. However, when I use attributes(object) I get the message $tframe Error: couldn't find function "print.tframe" It's as if <primitive: attributes> is not looking through the search list? Any suggestions?

Hello All, If I refer to a variable 'x', 'x' will be searched in the current frame or the parent frame, or the parent of the parent frame, etc., until it is found (or not found at all)? Could you please show me what code in R source that handles this? Is it in the C code or the R code? Thanks, Tom

I did some poking around with GDB and confirmed that the advice of setting R_CStackLimit after init, which is echoed in the "threading issues" section of the R-exts help document, isn't entirely useful because init apparently loads the main package which trips over the broken stack checking. Stack trace: #0 R_SignalCStackOverflow (usage=140732197147604) at errors.c:81 #1

Hi, forget about the below details. It is not related to the fact that the function is returned from a function. Sorry about that. I've been troubleshooting soo much I've been shoting over the target. Here is a much smaller reproducible example: x <- 1:10 y <- 1:10 + rnorm(length(x)) sp <- smooth.spline(x=x, y=y) ypred <- predict(sp$fit, x) # [1] 2.325181 2.756166 ...

Dear All, I've read the manual on "Writing R Extensions" and in particular the part on calling R from C. (Most of the manual is about calling C from R, not the other way around.) The good news is that I can now call _some_ R from C, specifically the R functions which have C header files. However it isn't clear to me how to call R functions that are written in R. I

I am wishing to write my own random distribution simulation function using C programmin language(for speed) via R. I am familiar with R programming but somewhat new to C programming. I was trying to understand "Writing R extensions" -guide and its part 6.16, but I found it hard to understand(http://cran.r-project.org/doc/manuals/R-exts.html#Standalone-Mathlib). I also tried to get

I've continued to work on speeding up R, and now have a collection of fourteen patches, some of which speed up particular functions, and some of which reduce general interpretive overhead. The total speed improvement from these patches is substantial. It varies a lot from one R program to the next, of course, and probably from one machine to the next, but speedups of 25% can be expected in

I've continued to work on speeding up R, and now have a collection of fourteen patches, some of which speed up particular functions, and some of which reduce general interpretive overhead. The total speed improvement from these patches is substantial. It varies a lot from one R program to the next, of course, and probably from one machine to the next, but speedups of 25% can be expected in