Interpretable Matching for Causal Inference
c(person(given = 'Vittorio',
family = 'Orlandi',
role = c('aut', 'cre'),
email = 'almost.matching.exactly@gmail.com'),
person(given = 'Sudeepa',
family = 'Roy',
role = 'aut',
email = 'sudeepa@cs.duke.edu'),
person(given = 'Cynthia',
family = 'Rudin',
role = 'aut',
email = 'cynthia@cs.duke.edu'),
person(given = 'Alexander',
family = 'Volfovsky',
role = 'aut',
email = 'alexander.volfovsky@duke.edu'))
Efficient implementations of the algorithms in the
Almost-Matching-Exactly framework for interpretable matching in causal
inference. These algorithms match units via a learned, weighted Hamming
distance that determines which covariates are more important to match on.
For more information and examples, see the Almost-Matching-Exactly website.
dplyr, magrittr, mice, glmnet, gmp, rlang, tidyr, xgboost,
devtools
Vittorio Orlandi [aut, cre],
Sudeepa Roy [aut],
Cynthia Rudin [aut],
Alexander Volfovsky [aut]

knitr, mice, rmarkdown, testthat, xgboost

nnet, knitr, mice, rmarkdown, testthat, xgboost

Core package of FLR, fisheries modelling in R.
FLR Team and various contributors. Initial design by Laurence T. Kell & Philippe Grosjean.
FLCore contains the core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at http://flr-project.org/, including a development mailing list.
methods, R(>= 2.1.0), graphics, stats, lattice(>= 0.12)
FLQuant.R FLQuants.R FLAccesors.R FLStock.R FLStocks.R io.FLStock.R FLBiol.R FLBiols.R FLFleet.R FLFleets.R FLIndex.R FLIndices.R io.FLIndices.R FLSR.R zzz.R

methods, R(>= 2.3.0), graphics, stats, lattice(>= 0.13-8)
FLQuant.R FLQuants.R FLAccesors.R FLStock.R FLStocks.R io.FLStock.R FLBiol.R FLBiols.R FLCatch.R FLFleet.R FLFleets.R FLIndex.R FLIndices.R io.FLIndices.R FLSR.R zzz.R

methods, R(>= 2.4.0), graphics, stats, lattice(>= 0.13-8)

Develops several procedures to explore fisheries data