similar to: How to package cran package into a debian package so that it can be installed using apt-get

dear dirk, i am following up on our thread on r-help-mailinglist. you advised me to bring it over here. when installing packages from within R, they usually go to a directory defined in R_LIBS_USER, which i have it set in .Renviron. when installing packages via sudo apt install r-cran-.., they go to /usr/lib/R/site-library. is there a possibility to have packages installed to the R_LIBS-USER

Apologies for my previous effort in HTML which apparently was scrubbed Dear R-users I wonder if I could get advice on the above problem I have just installed V 2.12.0 (I chose only the 32-bit version) into a new directory (C:/R) on a 64bit Windows 7 machine > sessionInfo() R version 2.12.0 (2010-10-15) Platform: i386-pc-mingw32/i386 (32-bit) locale: [1] LC_COLLATE=English_Australia.1252

I just upgraded from Linux Mint 20 to 21 and am no longer able to add the CRAN Ubuntu repository to my list of repositories. ?I am getting: $> sudo /usr/bin/add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" $> sudo apt update Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease Hit:2 http://archive.ubuntu.com/ubuntu jammy

When I set up my debian linux (unstable) system, the only R debian packages were on CRAN, so I followed directions and added the last line to my /etc/apt/sources.list euclid: # m /etc/apt/sources.list deb http://debian.yorku.ca/debian/ unstable main non-free contrib deb http://debian.yorku.ca/debian/non-US unstable/non-US main contrib non-free deb http://hexamon.ccs.yorku.ca/ unixteam-debs/

Good day all, I'm trying to plot a continuous line plot, which is followed by two forecast points eg. one forecast point is 12 months out, and another 24 months out from the last date of the line plot. In my attempts so far, the second plot (the forecast points) is scaled against a new axis scale, thus the two plots are not directly comparable (I need the forecast points to be scaled

I have a large Excel file with SKU numbers (stock keeping units) and forecasts which can be mimicked with the following: Period <- c(1, 2, 3, 1, 2, 3, 4, 1, 2) SKU <- c("A1","A1","A1","X4","X4","X4","X4","K2","K2") Forecast <- c(99, 103, 128, 63, 69, 72, 75, 207, 201) PeriodSKUForecast <-

hi: I am trying to fit prediction intervals for an arima object. My search led me to the link: http://finzi.psych.upenn.edu/R/library/forecast/html/forecast.Arima.html which has the function "forecast", as I wanted. However, when I try to run it in R, I get the message: Error in plot(forecast(fit)) : could not find function "forecast" Even the example provided on the page

I have a data frame that reads client ID date transcations 323232 11/1/2010 22 323232 11/2/2010 0 323232 11/3/2010 missing 121212 11/10/2010 32 121212 11/11/2010 15 ................................. I want to order the rows by client ID and date and using a black-box forecasting method create the data fcst(client,date of forecast, date for which forecast applies). Assume that I

Hello all, Does anyone know how to constrain/force specific coefficients when running lm()? I need to run recresid() {strucchange package} on the residuals of forecast.lm, but forecast.lm's coefficients must be determined by parameter.estimation.lm I could estimate forecast.lm without lm() and use some other kind of optimisation, but recresid() requires an object with class lm.

Hello, I am using forecast() in the forecast package to predict future values of an ARIMA model fit to a time series. I have read most of the documentation for the forecast package, but I can't figure out how to obtain the forecast variance for the predicted values. I tried using the argument "se.fit=TRUE," hoping this would work since forecast() calls predict(). Is there an easy

Hi all, We followed some books and sample codes and did some EMM estimation, only to find it won't be able to generate forecast. This is because in the stochastic volatility models we are estimating, the volatilities are latent variables, and we want to forecast 1-step ahead or h-step ahead volatilities. So it is nice to have the system estimated, but we couldn't get it to forecast at

There appears to be a problem with name matching in list assignment: Version 0.63.0 (November 14, 1998) ... > r <- list() > r$forecast.cov.trend <- 1:12 > r$forecast.cov.zero <- 1:12 > r$forecast.cov <- 1:2 > length(r$forecast.cov) [1] 0 #should be 2 > But note that this works correctly: > r <- list() >

hi all. i'm busy with some time series data, starting from an earlier period until the current day. i have created a time series forecast taking into account the entire data from the earlier date up until 2007, using the "forecast" package for R. i am comparing this forecasted data to the actual/ observed data (which starts from the earlier date up until the current day). my

My guess would be that if you inspect the output from ma(dat3[1:28], order=3) you will find some NAs in it. And then forecast() doesn't like NAs. But I can't check, because I can't find the ma() and forecast() functions. I assume they come from some package you installed; it would be helpful to say which package. -Don -- Don MacQueen Lawrence Livermore National Laboratory 7000

Dear all, I stumbled upon what appears to be a troublesome issue when sampling from an ARIMA model (from Rob Hyndman's excellent 'forecast' package) that contains a seasonal AR component. Here's how to reproduce the issue. (I'm using R 2.9.2 with forecast 2.19; see sessionInfo() below). First some data: > x <- c( 0.132475, 0.143119, 0.108104, 0.247291, 0.029510,

dear members, I have a heteroscedastic time series which I want to transform to make it homoscedastic by a box cox transformation. I am using Otexts by RJ hyndman and George Athanopolous as my textbook. They discuss transformation and also say the fpp3 and the fable package automatically back transforms the point forecast. they also discuss the process which I find to be

Hi, I would like to create variables in R containing parameters of summary(*Forecast Results*). Using the following code: library(forecast) data <- AirPassengers xets <- ets(data, model="ZZZ", damped=NULL) xfor <- forecast(xets,h=12, level=c(80,95)) summary(xfor) the output is: Forecast method: ETS(M,A,M) Model Information: ETS(M,A,M) Call: ets(y = data, model =

(My apologies if this is a repeated posting. I couldn't find any trace of my previous attempt in the archive.) I'm having trouble with forecast() in the dse2 package. It works fine for me on a model without a trend, but gives me NaN output for the forecast values when using a model with a trend. An example: # Set inputs and outputs for the ARMA model fit and test periods

You are right that there are no NAs in the practice data. But there are NAs in the moving average data. To see this, break your work into two separate steps, like this: tnr.ma <- ma(dat3[1:28], order=3) TNR_moving_average <- forecast(tnr.ma, h=8) I think you will find that the warning comes from the second step. Print tnr.ma and you will see some NAs. -Don -- Don MacQueen Lawrence

Hello everybody! I have an ARIMA model for a time series. This model was obtained through an auto.arima function. The resulting model is a ARIMA(2,1,4)(2,0,1)[12] with drift (my time series has monthly data). Then I perform a 12-step ahead forecast to the cited model... so far so good... but when I look the plot of my forecast I see that the result is really far from the behavior of my time