Residual balancing is a robust method of constructing weights for
marginal structural models, which can be used to estimate (a) the average treatment effect in
a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation
analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020
<doi:10.1017/pan.2020.2>). This package provides three functions, rbwPoint(), rbwMed(), and rbwPanel(),
that produce residual balancing weights for estimating (a), (b), (c), respectively.
CBPS, ebal, ipw, kableExtra, knitr, survey, rmarkdown,
testthat (>= 3.0.0)

ebal, knitr, survey, rmarkdown, testthat (>= 3.0.0)

Robust and Conditional Data Envelopment Analysis (DEA)
person(given = "Anna",
family = "Mergoni",
role = c("aut", "cre"),
email = "anna.mergoni@kuleuven.be",
comment = c(ORCID = "0000-0002-9128-5341"))
With this package we provide an easy method to compute robust and conditional Data Envelopment Analysis (DEA),
Free Disposal Hull (FDH) and Benefit of the Doubt (BOD) scores.
The robust approach is based on the work of Cazals, Florens and Simar (2002) <doi:10.1016/S0304-4076(01)00080-X>.
The conditional approach is based on Daraio and Simar (2007) <doi:10.1007/s11123-007-0049-3>.
Besides we provide graphs to help with the choice of m.
We relay on the 'Benchmarking' package to compute the efficiency scores and on the 'np' package to compute non parametric estimation of similarity among units.

Regionalization of Multiscale Spatial Processes
Jonathan R. Bradley [aut],
Christopher K. Wikle [aut],
Scott H. Holan [aut],
Shannon T. Holloway [aut, cre]
c(person("Jonathan R.", "Bradley", role = "aut"),
person("Christopher K.", "Wikle", role = "aut"),
person("Scott H.", "Holan", role = "aut"),
person("Shannon T.", "Holloway", role = c("aut", "cre"),
email = "sthollow@ncsu.edu"))
Regionalization of multiscale spatial processes based on a criterion for spatial aggregation error. The multiscale representation is a truncated Karhunen-Loaeve expansion using Obled-Creutin eigenfunctions. The method is incorporated within a Bayesian framework using an MCMC implementation of a latent spatial model. Bradley, J. R., Wikle, C. K., and Holan, S. H. (2017) <doi:10.1111/rssb.12179>.
ClustGeo, MASS, fields, ggplot2, methods, pracma, raster,
rgeos, sp, gridExtra, rlang, Matrix, sf, LaplacesDemon
'knots.R' 'GBFObj.R' 'semiDefinite.R' 'miPrior.R'
'MIpriorInv.R' 'checkInputs.R' 'checkDB.R' 'checkNW.R'
'checkSpatialData.R' 'generatingBasis.R' 'getArea.R'
'wMatrix.R' 'choleskyInvW.R' 'obledCruetinBasis.R' 'OCbasis.R'
'QPriorObj.R' 'QPriorObj_MI.R' 'QPriorObj_Wishart.R'
'biSquare.R' 'cage.R' 'countyExampleData.R' 'yOpt.R'
'hMatrix.R' 'dCCAGE.R' 'gibbs.R' 'verifyQPrior.R'
'verifyffdir.R' 'verifyCovariates.R' 'verifyNumericVector.R'
'inputPrep.R' 'summaryInfo.R' 'map.R' 'minCAGE.R' 'minCAGESH.R'
'oceanExampleData.R' 'optimalDCAGE.R' 'optRegion.R'
'plot.rcage.R' 'radial.R' 'region.R' 'wendland.R'
'windExampleData.R' 'yCageSource.R' 'yFinest.R'