Forward search for Mokken scale analysis
fwdmsa performs the Forward Search for Mokken scale
analysis. It detects outliers, it produces several types of
diagnostic plots.

Fire Weather Index System and Fire Behaviour Prediction System
Calculations
Xianli Wang, Alan Cantin, Marc-Andr&eacute; Parisien, Mike
Wotton, Kerry Anderson, and Mike Flannigan
This package provides three new functions to calculate the
outputs of the two main components of the Canadian Forest Fire
Danger Rating System (CFFDRS): the Fire Weather Index (FWI)
System and the Fire Behaviour Prediction (FBP) System.

Xianli Wang, Alan Cantin, Marc-Andre Parisien, Mike Wotton,
Kerry Anderson, and Mike Flannigan

Xianli Wang, Alan Cantin, Marc-Andre Parisien, Mike Wotton, Kerry Anderson, and Mike Flannigan
This package provides three new functions to calculate the outputs of the two main components of the Canadian Forest Fire Danger Rating System (CFFDRS): the Fire Weather Index (FWI) System and the Fire Behaviour Prediction (FBP) System.

Provides three functions to calculate the outputs of the two main components of the Canadian Forest Fire Danger Rating System (CFFDRS): the Fire Weather Index (FWI) System and the Fire Behaviour Prediction (FBP) System.

Fast Wild Cluster Bootstrap Inference for Linear Models
c(
person(given = "Alexander",
family = "Fischer",
role = c("aut", "cre"),
email = "alexander-fischer1801@t-online.de"),
person(given = "David",
family = "Roodman",
role = "aut"),
person(given = "Achim",
family = "Zeileis",
role = c("ctb"),
comment = "Author of included sandwich fragments"),
person(given = "Nathaniel",
family = "Graham",
role = c("ctb"),
comment = "Contributor to included sandwich fragments"),
person(given = "Susanne",
family = "Koell",
role = c("ctb"),
comment = "Contributor to included sandwich fragments"),
person("Laurent",
"Berge",
role = c("ctb"),
comment = "Author of included fixest fragments"),
person("Sebastian",
"Krantz",
role = c("ctb"))
)
Implementation of fast algorithms for wild cluster bootstrap
inference developed in 'Roodman et al' (2019, 'STATA' Journal,
<doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022),
which makes it feasible to quickly calculate bootstrap test
statistics based on a large number of bootstrap draws even for
large samples. Multiple bootstrap types as described in 'MacKinnon,
Nielsen & Webb' (2022) are supported.
Further, 'multiway' clustering, regression weights,
bootstrap weights, fixed effects and 'subcluster' bootstrapping
are supported. Further, both restricted ('WCR') and unrestricted
('WCU') bootstrap are supported. Methods are provided for a variety
of fitted models, including 'lm()', 'feols()'
(from package 'fixest') and 'felm()' (from package 'lfe').
Additionally implements a 'heteroskedasticity-robust' ('HC1') wild
bootstrap.
Further, the package provides an R binding to 'WildBootTests.jl',
which provides additional speed gains and functionality,
including the 'WRE' bootstrap for instrumental variable models
(based on models of type 'ivreg()' from package 'ivreg')
and hypotheses with q > 1.
collapse, dreamerr, Formula, generics, dqrng, gtools, Matrix,
JuliaConnectoR, MASS, Rcpp, summclust
fixest, lfe, ivreg, clubSandwich, lmtest, data.table,
fabricatr, covr, knitr, rmarkdown, broom, modelsummary, bench,
testthat (>= 3.0.0), tibble, sandwich
Julia (>= 1.7), WildBootTests.jl (>=0.8.1)
Alexander Fischer [aut, cre],
David Roodman [aut],
Achim Zeileis [ctb] (Author of included sandwich fragments),
Nathaniel Graham [ctb] (Contributor to included sandwich fragments),
Susanne Koell [ctb] (Contributor to included sandwich fragments),
Laurent Berge [ctb] (Author of included fixest fragments),
Sebastian Krantz [ctb]