Title: Multiple Imputation using Denoising Autoencoders
Description: A tool that allows users to impute missing data with 'MIDAS', a multiple imputation method using denoising autoencoders as documented in Lall and Robinson (2020) <doi:10.33774/apsa-2020-3tk40-v3>. This method has significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when run on large datasets with many columns or categories. Alongside interfacing with 'Python' to run the core algorithm, this package contains tools to process the data before and after model training, run imputation model diagnostics, generate multiple completed datasets, and estimate multiply-imputed regression models.
Depends: R (>= 3.6.0), data.table, mltools, reticulate

Title: Multiple Imputation with Denoising Autoencoders
Description: A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks. This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets.
Packaged: 2020-11-02 10:19:04 UTC; tomrobinson

Packaged: 2021-01-30 00:25:27 UTC; tomrobinson

Packaged: 2022-02-09 21:10:24 UTC; tomrobinson

Title: Estimates Pareto-Optimal Solution for Hiring with 3 Objectives
person("Chelsea", "Song", , "qianqisong@gmail.com", role = c("aut", "cre"),
Description: Estimates Pareto-optimal solution for personnel selection
with 3 objectives using Normal Boundary Intersection (NBI) algorithm
Takes predictor intercorrelations and predictor-objective relations as
input and generates a series of solutions containing predictor weights
as output. Accepts between 3 and 10 selection predictors. Maximum 2
objectives could be adverse impact objectives. Partially modeled after
'ParetoR' package described in Song et al. (2017)
<doi:10.1037/apl0000240>. For details, see Song et al. (in press).
Imports: graphics, grDevices, nloptr, stats
Author: Chelsea Song [aut, cre] (<https://orcid.org/0000-0003-0910-1281>)