Calculates 15 different goodness of fit criteria. These are; standard deviation ratio (SDR), coefficient of variation (CV), relative root mean square error (RRMSE), Pearson's correlation coefficients (PC), root mean square error (RMSE), performance index (PI), mean error (ME), global relative approximation error (RAE), mean relative approximation error (MRAE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), coefficient of determination (R-squared), adjusted coefficient of determination (adjusted R-squared), Akaike's information criterion (AIC), corrected Akaike's information criterion (CAIC), Mean Square Error (MSE), Bayesian Information Criterion (BIC) and Normalized Mean Square Error (NMSE).

Enhanced Help to Enable "Docstring"-Comments in Users Functions
person(given = "Marcelo",
family = "Ponce",
role = c("aut", "cre"),
email = "mponce@scinet.utoronto.ca")
By overloading the R help() function, this package allows users to use "docstring" style comments within their own defined functions.
testthat (>= 2.1.0), knitr, rmarkdown, crayon

By overloading the R help() function, this package allows users to use "docstring" style comments within their own defined functions. The package also provides additional functions to mimic the R basic example() function and the prototyping of packages.

Gary King <king@harvard.edu>, Molly Roberts <molly.e.roberts@gmail.com>
mvtnorm, msm, tmvtnorm, ellipse, plotrix, MASS, ucminf,
cubature, mnormt, foreach, sp
Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.

mvtnorm, msm, tmvtnorm, ellipse, plotrix, MASS, ucminf,
cubature, mnormt, foreach, rgl, sp

Real Datasets for Assessing Ecological Inference Algorithms
person(given = "Jose M.",
family = "Pavía",
role = c("aut", "cre"),
email = "jose.m.pavia@uv.es",
comment = c(ORCID = "0000-0002-0129-726X"))
Provides more than 550 data sets of actual election results.
Each of the data sets includes aggregate party and candidate outcomes at the voting
unit (polling stations) level and two-way cross-tabulated results at the district level.
These data sets can be used to assess ecological inference algorithms devised
for estimating RxC (global) ecological contingency tables using exclusively aggregate results
from voting units.