Other packages > Find by keyword >

moose  

Mean Squared Out-of-Sample Error Projection
View on CRAN: Click here


Download and install moose package within the R console
Install from CRAN:
install.packages("moose")

Install from Github:
library("remotes")
install_github("cran/moose")

Install by package version:
library("remotes")
install_version("moose", "0.0.1")



Attach the package and use:
library("moose")
Maintained by
Chris Rohlfs
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-09-09
Latest Update: 2022-09-09
Description:
Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) . It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.
How to cite:
Chris Rohlfs (2022). moose: Mean Squared Out-of-Sample Error Projection. R package version 0.0.1, https://cran.r-project.org/web/packages/moose. Accessed 05 Aug. 2026.
Previous versions and publish date:
No previous versions
Other packages that cited moose R package
View moose citation profile
Other R packages that moose depends, imports, suggests or enhances
Complete documentation for moose
Functions, R codes and Examples using the moose R package
Some associated functions: moose . 
Some associated R codes: moose.R .  Full moose package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

noisyr  
Noise Quantification in High Throughput Sequencing Output
Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are use ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
dcov  
A Fast Implementation of Distance Covariance
Efficient methods for computing distance covariance and relevant statistics. See Sz ...
Download / Learn more Package Citations See dependency  
dhReg  
Dynamic Harmonic Regression
Building and forecasting time series data with multiple seasonality using Dynamic Harmonic Regressio ...
Download / Learn more Package Citations See dependency  
kernelPSI  
Post-Selection Inference for Nonlinear Variable Selection
Different post-selection inference strategies for kernelselection as described in kernelPSI a Post-S ...
Download / Learn more Package Citations See dependency  

28,083

R Packages

239,283

Dependencies

74,457

Author Associations

28,084

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA