similar to: Multivariate Laplace density

Does anyone know of a package that uses C code to calculate a multivariate normal density? My goal is to find a faster way to calculate MVN densities and avoid R loops or apply functions, such as when X and mu are N x K matrices, as opposed to vectors, and in this particular case, speed really matters. I would like to be able to use .C or .Call to pass X, mu, Sigma, and N to a C program and have

I would like to estimate a 95% highest density area for a multivariate parameter space (In the context of anova). Unfortunately I have only experience with univariate kernel density estimation, which is remarkebly easier :) Using Gibbs, i have sampled from a posterior distirbution of an Anova model with k means (mu) and 1 common residual variance (s2). The means are independent of eachother, but

Hi, I am looking for an efficient way to compute the values of the density function of a multivariate T distribution - something like "dmvnorm", but for T distr. Does this exist somewhere? Many thanks, Jan Bulla Goettingen University

One of the real advances (in my humble oppinion of course) of 2003 is the Very Fast Multivariate Kernel Density Estimation algorithm by Alex Gray which achieves several order of speed improvement by using Computational Geometry to organize the data. The algorithm is now implemented in C++ with Mathlab interface by Alexander Ihler of MIT: http://ssg.mit.edu/~ihler/code/kde.shtml I wondered if a

Does anybody know of a function that implements the derivative (gradient) of the multivariate normal density with respect to the *parameters*? It?s easy enough to implement myself, but I?d like to avoid reinventing the wheel (with some bugs) if possible. Here?s a simple example of the result I?d like, using numerical differentiation: library(mvtnorm) library(numDeriv) f=function(pars, xx, yy)

Hello, I'm using dmnorm from the package {mnormt} and getting strange results. First, according to the documentation, dmnorm should return a vector of densities, and I'm only getting one value returned (which is what I would expect). I've been interpreting this as the joint density of all values in the x vector (which is what I want). Should a vector of densities be returned, and if

Hi, I need to estimate the density at the mean of a sample of a few thousands data points with a dimesion up to 5. The data is uni-modal and regularly shaped. I couldn't find any kernel density package for R which supports more than 3 dimensions. Have I overlooked a package or does somebody have code for this purpose? Any other advice? Regards, Stephan

All, I am working with a large dataset that tracks who watches what media at what time. I am working on media consumption data from a survey, and one of the batteries asks if [r] has consumed a particular form of media at X time over the course of a week, so the data looks like this: 5:00-5:30 [0,1] 5:30-6:00 [0,1]. 23:30-0:00

r-devel list, I have recently written an R package that solves a linear least squares problem, and computes the multivariate normal density function. The bulk of the code is written in C, with interfacing code to the BLAS and Lapack libraries. The motivation here is speed. I ran into a problem computing the determinant of a symmetric matrix in packed storage. Apparently, there are no explicit

Hi, I would like to know, if there's any R function, which allows calculation of probability values (0,1) from multivariate normal densities. I would be grateful for any output. Cheers, MG

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

Hello, I have a n*2 matrix, called "plan", which contains n observations from 2 variates. I want a kernel density estimate of the joint distribution of these 2 variates. I try : density(plan). Unfortunately, R thinks there is 2n observations (if n=10, 20 observations), where there is only n. How to to make a multivariate kernel density estimate ? Thank you very much.

I have spatial data in 2 dimensions - say (x,y). The correlation between x and y is fairly substantial. My goal is to use a non-parametric approach to estimate the multivariate density describing the spatial locations. Ultimately, I would like to use this estimated density to determine the area associated with a 95% probability contour for the data. Given the strong correlation between x and

Hi Spencer, I have seen your name on the web site, and perhaps you can help me with my R problem. I'm trying to use KernSur to put in evidence a substructure in a bidimensional plot. My problem is that, in order to get the density in the low density areas (in which the substructure is located) I should use different bandwidths. How I can do that? Also, I think that the best choice for my

hello sorry for my english I would like estimate density for multivariate variable,( f(x,y) , f(x,y ,z) for example) ; for calculate mutual information how is posible with R? thanks Bernard Bernard Palagos Unité Mixte de Recherche Cemagref - Agro.M - CIRAD Information et Technologie pour les Agro-Procédés Cemagref - BP 5095 34033 MONTPELLIER Cedex 1 France

Hello, I am a newbie to R and the subject of density estimation in two dimensions or more. I would like to have some advice concerning a comparison between the R packages for density estimation in bivariate or higher order problems; I mean explicitly the packages: 1) ash 2) KernSmooth 3) locfit 4) sm. My specific problem now is having a set of numerical pairs (x_i, y_i), arising from a

Hello, <http://tolstoy.newcastle.edu.au/R/help/04/07/0312.html#0313qlink1> Could anybody tell me how to generate discrete Laplacian distribution? I need to sample uma discretised Laplacian density like this: J( g -> g´) ~ exp (-lambda | g´ - g |) g in {0,…, gmax} Thanks, Nicolette [[alternative HTML version deleted]]

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects

Does anyone have any R code that shows how to do a Laplace Approximation? I know there are a variety of these numerical approximation algorithms and I'm pretty open at this point, I'm just curious how it's approximated in R code. I have seen some functions in packages, but I think they all call C code, and am looking for an example in R only. Thanks. -- View this message in

Dear everyone, I'm coding the Horowitz-Spokoiny (2001) test [1], and I would be very grateful or some advice regarding the Kernel density (apologies beforehand if my terminology is not fully correct). I have looked into ksmooth and npreg, but with no success. Given a (n x p) matrix of covariates X, I need to construct the following matrix of Kernel densities or weights: w(x_i, x_j) =