An algorithm for reducing errors-in-variable bias in simple
linear regression
Mehmet Hakan Satman (PhD), Erkin Diyarbakirlioglu (PhD)
EIVE performs a compact genetic algorithm search to reduce
errors-in-variables bias in linear regression.

EIVE performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression.

An Algorithm for Reducing Errors-in-Variable Bias in Simple
Linear Regression
Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.

Mehmet Hakan Satman (Ph.D.), Erkin Diyarbakirlioglu (Ph.D.)

R (>= 3.4.0), stats, grDevices, graphics, utils, R2jags
This includes functions for analysis with error-prone covariates, including deconvolution, latent regression and errors-in-variables regression.  It implements methods by Rabe-Hesketh et al. (2003) <doi:10.1191/1471082x03st056oa>, Lockwood and McCaffrey (2014) <doi:10.3102/1076998613509405>, and Lockwood and McCaffrey (2017) <doi:10.1007/s11336-017-9556-y>, among others.

Ecological Inference with individual and aggregate data
This package allows to use the Multinomial-Dirichlet-Model of
Ecological Inference for estimating inner Cells of RxC-Tables. This was
already implemented in the eiPack-package. eiwild-package now has the
possibility to use individual level data to support the aggregate level
data and using different Hyperpriori-Distributions.

This package allows to use the hybrid Multinomial-Dirichlet-Model of
Ecological Inference for estimating inner Cells of RxC-Tables. This was
already implemented in the eiPack-package. eiwild-package now has the
possibility to use individual level data to support the aggregate level
data and using different Hyperpriori-Distributions.

This package allows to use the hybrid Multinomial-Dirichlet-Model
of Ecological Inference for estimating inner Cells of RxC-Tables. This was
already implemented in the eiPack-package. eiwild-package now has the
possibility to use individual level data to support the aggregate level
data and using different Hyperpriori-Distributions.