similar to: maximum likelihood estimate

R-devel, I'm interested in looking at some examples of existing R packages that rely heavily on S4 classes to get a feel for varying styles and package organization techniques. Could you recommend any packages that might serve as a good starting point? Thanks in advance, Jeff

R-devel, I'm interested in looking at some examples of existing R packages that rely heavily on S4 classes to get a feel for varying styles and package organization techniques. Could you recommend any packages that might serve as a good starting point? Thanks in advance, Jeff

I'm in the process of converting some S3 methods to S4 methods. I have this function : setGeneric("enrichmentCalc", function(rs, organism, seqLen, ...){standardGeneric("enrichmentCalc")}) setMethod("enrichmentCalc", c("GenomeDataList", "BSgenome"), function(rs, organism, seqLen, ...) { ... ... ... })

Hi, I got the following problem. I am doing a maximum likelihood estimation for a Kalman Filter. For this purpose, I have to invert an error matrix Ffast of dimension "no. parameters X no.parameters". The usualy optim methods often find only local minima, so I decided to make the optimization using the SANN algorithm, which is very slow already. However, this becomes a real problem

Dear R users and Dear authors of the distr package and sequels I am trying to use the (very nice) package distrMod as I want to implement maximum likelihood (ML) fit of some univariate data for which I have derived a theoretical continuous density (pdf). As it is a parametric density, I guess that I should implement myself a new distribution of class AbscontDistributions (as stated in the pdf

On Tue, Oct 6, 2015 at 4:58 PM, Adrian Du?a <dusa.adrian at unibuc.ro> wrote: > Hi Gabriel, > > On Tue, Oct 6, 2015 at 10:59 PM, Gabriel Becker <gmbecker at ucdavis.edu> > wrote: > >> [...] >> >> At the very least, this is seems to be a flagrant violation of the >> *spirit* of the CRAN policy, which AFAIK is intended to enforce >>

After some reorganization, we would like to announce the availability on CRAN of a new version (1.6) of our package "distr" as well as the availabilty on CRAN of the new packages "distrEx", "distrSim", "distrTEst", "RandVar". ----------------------------------------------------------------------------------------- Changes from 1.5 to 1.6 -[former

After some reorganization, we would like to announce the availability on CRAN of a new version (1.6) of our package "distr" as well as the availabilty on CRAN of the new packages "distrEx", "distrSim", "distrTEst", "RandVar". ----------------------------------------------------------------------------------------- Changes from 1.5 to 1.6 -[former

Hello. I'm a newbie here. In my script (I name it readData.R), I wrote the followings: readData <-function(){ med = read.csv("medicalData.csv");} Then I tested the script by 'Source R Code' then on the command I typed 'readData()' then I typed 'med' to check if the variable contains the medical data but it returned 'Object med is not found'. What

Hello. I'm trying to compute median for a filtered column based on other column but there was something wrong. I'll show how I did step by step. Here's the data: a b c class 1 12 0 90 A-B2 3 97 11 A-B3 78 NA 123 A-C4 NA NA 12 A-C5 8 33 2 A-B6 12 NA 0 A-D On the command I typed: 1) data =

Dear R-users, I have the following 2 files; A V1 V2 A 1 A 2 A 3 A 4 B 1 B 4 C 1 C 3 D 4 B V1 V2 process1 1 process2 2 process3 3 process4 4 I want to get this output C V1 V2 V3 A 1 process1 A 2 process2 A 3 process3 A 4 process4 B 1 process1 B

Greetings R-users, I have been using the fpc package in R to cluster my data. Speficically I am using kmeansruns clustering. I would like to know how I use R to partition data into clusters. What I am doing is as follows. # Use csv file as input ##################### wholeset = read.csv("Spellman800genesImputed.csv") # exclude first col (gene names) ##########################

We would like to announce the availability on CRAN of new versions of our packages "distr" (1.8), "distrEx" (0.4-4), "distrSim" (1.8), "distrTEst" (1.8), "startupmsg" (0.4) as well as a new package "distrDoc" (1.8) [all of them require R >= 2.2.0] -----------------------------------------------------------------------------------------

We would like to announce the availability on CRAN of new versions of our packages "distr" (1.8), "distrEx" (0.4-4), "distrSim" (1.8), "distrTEst" (1.8), "startupmsg" (0.4) as well as a new package "distrDoc" (1.8) [all of them require R >= 2.2.0] -----------------------------------------------------------------------------------------

We would like to announce the availability on CRAN (with possibly a minor delay until on every mirror) of new versions of our packages in the "distrXXX"-family (version 1.9), i.e.; "distr", "distrEx", "distrSim", "distrTEst", and "distrDoc" as well as of package for managing startup messages, "startupmsg" (0.5). [all of

We would like to announce the availability on CRAN (with possibly a minor delay until on every mirror) of new versions of our packages in the "distrXXX"-family (version 1.9), i.e.; "distr", "distrEx", "distrSim", "distrTEst", and "distrDoc" as well as of package for managing startup messages, "startupmsg" (0.5). [all of

Dear R list, I have a function specifying my log-likelihood, and now I need to set the constraint that *alpha > kappa*, could anyone help me with setting this in my function? the function is defined as follows: mll <- function(param){ n <- length(x) psi <- numeric(n) psi[1] <- 1.0 a0 <- exp(param[1]); a1 <-exp(param[2]); b1 <- exp(param[3]); *alpha *<-

We would like to announce the availability on CRAN of new versions of our packages "distr" (1.7), "distrEx" (0.4-3), "distrSim" (1.7), "distrTEst" (1.7), as well as a new package "startupmsg" [all of them require R >= 2.2.0] ----------------------------------------------------------------------------------------- ************ new package

We would like to announce the availability on CRAN of new versions of our packages "distr" (1.7), "distrEx" (0.4-3), "distrSim" (1.7), "distrTEst" (1.7), as well as a new package "startupmsg" [all of them require R >= 2.2.0] ----------------------------------------------------------------------------------------- ************ new package

Hello, Consider MCMC sampling with metropolis / metropolis hastings proposals and a density function with a given valid parameter space. How are MCMC proposals performed if the parameter could be located at the very extreme of the parameter space, or even 'beyond that' ? Example to express it and my very nontechnical 'beyond that': The von Mises distribution is a circular