Title: Automatic Time Series Analysis and Forecasting using the Ata

Title: Inference for Average Treatment Effects using Covariate
Author: Asad Haris <aharis@uw.edu> and Gary Chan <kcgchan@uw.edu>
Description: Nonparametric estimation and inference for average treatment effects based on covariate balancing.

Title: Run Allometric Trophic Networks Models
c(person("Benoit", "Gauzens", role = c("cre", "aut"), email = "benoit.gauzens@gmail.com"),
person("Emilio", "Berti", role = "aut"))
Description: Implements the differential equations associated to different versions of Allometric Trophic Models (ATN) to estimate the temporal dynamics of species biomasses in food webs. It offers several features to generate synthetic food webs and to parametrise models as well as a wrapper to the ODE solver deSolve.
Imports: Rcpp (>= 1.0.7), methods, stats, utils, deSolve
Suggests: rmarkdown, knitr, testthat, R.rsp

Authors@R: c(person("Jon", "Eugster", role = "ctr"),
person("Andrea", "Farnham", role = "ctr"),
person("Raphael", "Hartmann", role = "ctr"),
person("Gilles", "Kratzer", role = "ctr"),
person("Silvia", "Panunzi", role = "ctr"),
person("Sophie", "Schneider", role = "ctr"),
person("Torsten", "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org", comment = c(ORCID = "0000-0001-8301-0471")))
Description: Plot party trees in left-right orientation instead of the

Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)

Title: Bounding Treatment Effects by Limited Information Pooling
person("Sokbae", "Lee", email = "sl3841@columbia.edu", role = c("aut", "cre")),
person("Martin", "Weidner", email = "martin.weidner@economics.ox.ac.uk", role = "aut"))
Description: Estimation and inference methods for bounding average treatment effects (on the treated) that are valid under an unconfoundedness assumption.
The bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated.
This robustness is achieved by only using limited "pooling" of information across observations.
For more details, see the paper by Lee and Weidner (2021), "Bounding Treatment Effects by Pooling Limited Information across Observations," <arXiv:2111.05243>.
Suggests: knitr, rmarkdown, testthat, ggplot2
Packaged: 2021-11-24 15:56:00 UTC; sokbaelee