A Cross-Scale Analysis Tool for Model-Observation Visualization
and Integration
c(
person("Yannis", "Markonis", email = "imarkonis@gmail.com", role = c("aut", "cre")),
person("Christoforos", "Pappas", email = "christoforos.pappas@umontreal.ca", role = c("aut")),
person("Mijael", "Vargas", email = "vargas_godoy@fzp.czu.cz", role = c("ctb")),
person("Simon", "Papalexiou", email = "simon@uni.edu", role = c("ctb")),
person("Martin", "Hanel", email = "hanel@fzp.czu.cz", role = c("ctb"))
)
Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. 'csa' package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.
grDevices, stats, ggplot2, data.table, scales, reshape2,
moments, Lmoments, foreach, ggpubr, raster, doParallel,
parallel
Yannis Markonis [aut, cre],
Christoforos Pappas [aut],
Mijael Vargas [ctb],
Simon Papalexiou [ctb],
Martin Hanel [ctb]

Bounds on Distributional Treatment Effect Parameters
The joint distribution of potential outcomes is not typically identified under standard identifying assumptions such as selection on observables or even when individuals are randomly assigned to being treated. This package contains methods for obtaining tight bounds on distributional treatment effect parameters when panel data is available and under a Copula Stability Assumption as in Callaway (2017) <https://ssrn.com/abstract=3028251>.
stats, ggplot2, BMisc, pbapply, progress, qte