Title: An Algorithm for Reducing Errors-in-Variable Bias in Simple
Description: 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.

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

Depends: R (>= 3.4.0), stats, grDevices, graphics, utils, R2jags
Description: 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.

Title: Ecological Inference with individual and aggregate data
Description: 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.

Description: This package allows to use the hybrid Multinomial-Dirichlet-Model of

Description: This package allows to use the hybrid Multinomial-Dirichlet-Model
of Ecological Inference for estimating inner Cells of RxC-Tables. This was

Title: Export Emissions to Atmospheric Models
Description: Emissions are mass of pollutants released into the atmosphere. Air quality models needs emission data, with spatial and temporal distribution, to represent air pollutant concentrations. 'eixport' creates inputs for the air quality models Weather Research and Forecasting model coupled to Chemistry 'WRF-Chem' Grell et al (2005) <doi:10.1016/j.atmosenv.2005.04.027>, Brazilian Regional Atmospheric Modeling System with a simple photochemical module 'BRAMS-SPM' Freitas et al (2005) <doi:10.1016/j.atmosenv.2005.07.017> and  a line source dispersion model 'RLINE' Snyder et al (2013) <doi:10.1016/j.atmosenv.2013.05.074>.