README.md

In ncahill89/EIVmodels: Fit Errors-in-variables regression model

EIVmodels

In statistics, errors-in-variables (EIV) models, or measurement error models, are models that account for measurement errors in both the independent (predictor) and dependent (outcome) variables. EIVmodels is a R package designed specifically to account for measurement errors within some commonly used models (linear regression, change-point regression, (Integrated) Gaussian process regression) when analysing time-dependent data derived from paleoenvironmental reconstructions. The models are implemented in a Bayesian framework using the JAGS (Just Another Gibbs Sampler) software.

Requirements

• The package requires the installation of the JAGS software. Click to download JAGS.

Getting started

See Vignettes.

Installation

• This package is not currently on cran so you can download from Github. Make sure to have the devtools package installed and then execute the following:

devtools::install_github("ncahill89/EIVmodels")

ncahill89/EIVmodels documentation built on Dec. 5, 2022, 2:10 p.m.