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MultiRR  

Bias, Precision, and Power for Multi-Level Random Regressions
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("MultiRR", "1.1")



Attach the package and use:
library("MultiRR")
Maintained by
Yimen G. Araya-Ajoy
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-05-13
Latest Update: 2015-10-21
Description:
Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.
How to cite:
Yimen G. Araya-Ajoy (2015). MultiRR: Bias, Precision, and Power for Multi-Level Random Regressions. R package version 1.1, https://cran.r-project.org/web/packages/MultiRR. Accessed 13 Apr. 2025.
Previous versions and publish date:
1.0 (2015-05-13 17:03), 1.1 (2015-10-21 11:36)
Other packages that cited MultiRR R package
View MultiRR citation profile
Other R packages that MultiRR depends, imports, suggests or enhances
Complete documentation for MultiRR
Functions, R codes and Examples using the MultiRR R package
Some associated functions: Anal.MultiRR . Bias . Imprecision . MultiRR-package . Plot.Sim . Power . Sim.MultiRR . Summary . lmerAll . lower2 . mean2 . median2 . sd2 . upper2 . 
Some associated R codes: Anal.MultiRR.R . Bias.R . Imprecision.R . Plot.Sim.R . Power.R . Sim.MultiRR.R . lmerAll.R . lower2.R . mean2.R . median2.R . sd2.R . upper2.R .  Full MultiRR package functions and examples
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