similar to: [JOB] Job opportunity for compiling DSLs and automatic differentiation in finance

Hi there, I'm new to R and despite searching today, I can't find a function which will compute the adjoint of a matrix A. Does this adjoint function exist in R? Thanks in advance!

thanks a lot Renaud. but i was interested in Finney's fiducial confidence intervals of LD50 so to obtain comparable results with SPSS. But your reply leads me to the next question: does anybody know what is the best method (asymptotic, bootstrap etc.) for calculating confidence intervals of LD50? i could "get rid" of Finney's fiducial confidence intervals but

How do you calculate the percentage of data within 2SD, 3SD, 4SD, 5SD, and 6SD of the mean? I used the following link as the data I'm working with: nb10 <- read.table("http://www.adjoint-functors.net/su/web/314/R/NB10") if this helps answer my question. Can you please explain how to calculate the SD's? Please be specific in which part of the function changes when

Okay, so I have a homework projecr for R, and we had to input the following link as some sort of data: nb10 <- read.table("http://www.adjoint-functors.net/su/web/314/R/NB10"). Afterwards, we have to use fivenum(nb10) to find max, min, quantiles, and sd, but I'm okay with this. The next question is where I'm stuck. The question is as follows; Compare the median (use the

Howdee, One of my student spotted something I can't explain: a probability >1 vs a normal probability density function. > dnorm(x=1, mean=1, sd=0.4) [1] 0.9973557 > dnorm(x=1, mean=1, sd=0.39) [1] 1.022929 > dnorm(x=1, mean=1, sd=0.3) [1] 1.329808 > dnorm(x=1, mean=1, sd=0.1) [1] 3.989423 > dnorm(x=1, mean=1, sd=0.01) [1] 39.89423 > dnorm(x=1, mean=1, sd=0.001) [1]

Is there a way to apply a function, say cor(), to each combination of some number of variables, and this, without using loops? For example, I have day, hour, var1 and var2. How could I compute cor(var1,var2) for each day*hour combination and obtain a matrix with day, hour and the cor value for each combination? Thanks for your time, Marc =================== Marc Bélisle Professeur adjoint

Hello, I am beginning with R and I would like to test a non linear model. But I do not find exactly wath I am looking for in nls packages (or I do not know where to search). I would like to try a model like this : y=b * x exp(n)/(a exp(n) + x exp (n)) Where a = a0 + a1z b= b0 + b1z x and z are variables y the variable that I am trying to modelise a0, a1, b0 and b1 are parameters to determine. I

Howdee, My question appears at #6 below: 1. I want to model the growth of each of a large number of individuals using a 4-parameter logistic growth curve. 2. nlme does not converge with the random structure that I want to use. 3. nlsList does not converge for some individuals. 4. I decided to go around nlsList using: t(sapply(split(data, list(data$id)), function(subd){coef(nls(mass ~

Dear all, I am trying to avoid the warnings produced by: > x <- -2:2 > log(x) [1] NaN NaN -Inf 0.0000000 0.6931472 Warning message: production de NaN in: log(x) I thought that using ifelse would be a solution, but it is not the case: > ifelse(test = x < 0, yes = NaN, no = log(x)) [1] NaN NaN -Inf 0.0000000 0.6931472 Warning message: production

R / S Programmer - New York Description: Kaplan Test Prep & Admissions is looking for a R / S Programmer to join their research based in New York City. We are looking for highly motivated individuals to work in a customer-focused environment. This is a unique opportunity to develop with a leading educational company with a diverse series of statistical applications in education/learning,

Howdee, *** I know that the lmList() function exists, yet I don't want to use it. *** Would anyone be kind enough to tell how I can apply the function lm() to each level of a given factor so to obtain the intercept and slope for each factor level within a matrix? For instance, suppose a dataframe containing 3 variables: id, x and y. I want to compute the function lm() for each level

POSITION SUMMARY Postdoctoral Research Fellow at EcoHealth Alliance, New York, to study the evolution and ecology of zoonotic diseases from bats and other hosts. PRIMARY RESPONSIBILITIES The position is funded by a National Institutes of Health NIAID award to discover, characterize, and model the risk of new potential zoonoses from bats. The primary focus of the Research Fellow will be to model

Hi, Could anyone tell me what is the command used in R to do 1. Differentiation 2. Newton Raphson method (Numerical Analysis in general...) Are there any packages separately for this? Thanks for your help! BR, Shubha [[alternative HTML version deleted]]

Martin (see below) gives a good explanation of the difference between AD and symbolic differentiations. I'm of the opinion we can use both. However, the real issue as far as I'm concerned (from an optimizer's point of view, which may also be that of ODE and PDE folk) is that right now none of the offerings that we have are easy to use. Indeed, usability is one of the key issues in my

Dear all, I am the author of an open-source package for mathematical optimization and automatic differentiation called CasADi (www.casadi.org) and have recently started realize the potential of the LLVM project. At the core of CasADi are two fast interpretors for mathematical expressions and I'm now planning to complement these with JIT-compilation using LLVM. Does anyone know if there is

Dear all, I'm wondering if there are some ongoing projects for interior point methods in R (e.g. linking ipopt from Coin written in C++ to R) and for automatic differentiation in R (e.g. linking openAD available in C++ and Fortran) ? Many thanks, David [[alternative HTML version deleted]]

Hi R People: Is there a package for automatic differentiation, please? thanks in advance, Erin -- Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: erinm.hodgess at gmail.com

Hi, I have a scalar valued function with several variables. One of the variables is restricted to be non-negative. For example, f(x,y)=sqrt(x)*exp(y), then x should be non-negative. I need the gradient and hessian for some vector (0,y), i.e., I need the gradient and hessian at the boudary of parameter space. The "numderiv" package does not work, even for f(x)=sqrt(x), if you do

Hi, no-one else has said anything more pertinent so here's my two-pence. I have been thinking for a while about LLVM in the context of simulating _small_ stochastic systems by which I mean very much non-trivial stochastic transition functions, but still small enough that if compiled carefully down to machine code via LLVM with a good chance that they'll be faster. (With even

Could somebody share some tips on implementing multivariate integration and partial differentiation in R? For example, for a trivariate joint distribution (cumulative density function) of F(x,y,z), how to differentiate with respect to x and get the bivariate distribution (probability density function) of f(y,z). Or integrate f(x,y,z) with respect to x to get bivariate distribution of (y,z). Your