Inverse estimation is a classical and well-known problem in regression. In simple terms, it involves the use of an observed value of the response to make inference on the corresponding unknown value of the explanatory variable. To our knowledge, however, statistical software is somewhat lacking the capabilities for analyzing these types of problems. In this paper1 , we introduce investr (which stands for inverse estimation in R), a package for solving inverse estimation problems in both linear and nonlinear regression models.
Econometrics, SocialSciences, ChemPhys, Multivariate, Pharmacokinetics, Distributions, Environmetrics, Finance, NumericalMathematics, Optimization, Psychometrics, Robust, Survival, TimeSeries
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For attribution, please cite this work as
Greenwell & Kabban, "The R Journal: investr: An R Package for Inverse Estimation", The R Journal, 2014
BibTeX citation
@article{RJ-2014-009,
author = {Greenwell, Brandon M. and Kabban, Christine M. Schubert},
title = {The R Journal: investr: An R Package for Inverse Estimation},
journal = {The R Journal},
year = {2014},
note = {https://doi.org/10.32614/RJ-2014-009},
doi = {10.32614/RJ-2014-009},
volume = {6},
issue = {1},
issn = {2073-4859},
pages = {90-100}
}