Title: Multiplier Data Envelopment Analysis and Cross Efficiency
Author: Aurobindh Kalathil Puthanpura <kalat2@pdx.edu>
Maintainer: Aurobindh Kalathil Puthanpura <kalat2@pdx.edu>
Description: Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.

Imports: dplyr, ROI, ROI.plugin.glpk, ompr, ompr.roi

Title: Multi-Scale Geomorphometric Terrain Attributes
Description: Calculates multi-scale geomorphometric terrain attributes from regularly gridded digital terrain models using a variable focal windows size (Misiuk et al. (2021) <doi:10.1080/01490419.2021.1925789>; Wilson et al. (2007) <doi:10.1080/01490410701295962>; Wood (1996) <https://hdl.handle.net/2381/34503>).
Imports: Rcpp, raster, dplyr, shiny, rgl, stats, utils
Author: Alexander Ilich [aut, cre] (<https://orcid.org/0000-0003-1758-8499>),

Title: Causal Mediation Analysis in Multisite Trials
Depends: R (>= 3.1.0), lme4, statmod, Matrix
Description: We implement multisite causal mediation analysis using the methods proposed by Qin and Hong (in press). It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions.

Author: Xu Qin, Guanglei Hong, Jonah Deutsch, and Edward Bein
Depends: lme4, statmod, psych, MASS, ggplot2
Description: We implement multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) <doi:10.3102/1076998617694879> and Qin, Hong, Deutsch, and Bein (under review). It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. Because the identification assumptions are not always warranted, the package also provides a weighting-based balance checking function for assessing the remaining overt bias, as well as a weighting-based sensitivity analysis function for further evaluating the potential bias related to omitted confounding or to propensity score model misspecification.