Multiplier Data Envelopment Analysis and Cross Efficiency
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.

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

c(
person(given = "Alexander",
family = "Ilich",
role = c("aut", "cre"),
email = "ailich@usf.edu",
comment = c(ORCID = "0000-0003-1758-8499")),
person(given = "Vincent",
family = "Lecours",
role = c("aut")),
person(given = "Benjamin",
family = "Misiuk",
role = c("aut")),
person(given = "Steven",
family = "Murawski",
role = c("aut")))
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>).
Rcpp, raster, dplyr, shiny, rgl, stats, utils
Alexander Ilich [aut, cre] (<https://orcid.org/0000-0003-1758-8499>),
Vincent Lecours [aut],
Benjamin Misiuk [aut],
Steven Murawski [aut]

Causal Mediation Analysis in Multisite Trials
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.

Xu Qin, Guanglei Hong, Jonah Deutsch, and Edward Bein
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.