Functions for fitting GEV and POT (via point process fitting)
models for extremes in climate data, providing return values, return
probabilities, and return periods for stationary and nonstationary models.
Also provides differences in return values and differences in log return
probabilities for contrasts of covariate values. Functions for estimating risk
ratios for event attribution analyses, including uncertainty. Under the hood,
many of the functions use functions from 'extRemes', including for fitting the
statistical models.

c(person("Christopher", "Paciorek", email =
"paciorek@stat.berkeley.edu", role = c("aut", "cre")),
person("Harinarayan", "Krishnan", email = "hkrishnan@lbl.gov",
role = c("ctb")))
Functions for fitting GEV and POT (via point process fitting)
models for extremes in climate data, providing return values, return
probabilities, and return periods for stationary and nonstationary models.
Also provides differences in return values and differences in log return
probabilities for contrasts of covariate values. Functions for estimating risk
ratios for event attribution analyses, including uncertainty. Under the hood,
many of the functions use functions from 'extRemes', including for fitting the
statistical models. Details are given in Paciorek, Stone, and Wehner (2018)
<doi:10.1016/j.wace.2018.01.002>.
Christopher Paciorek [aut, cre],
Harinarayan Krishnan [ctb]

Statistical Methods for Climate Sciences
Absolute homogeneity tests SNHT absolute 1-breaks, 1-break,
SD different from 1, 2-breaks, Buishand, Pettitt, von Neumann ratio and
ratio-rank, Worsley, and Craddock, Relative homogeneity tests SNHT
absolute 1-breaks, 1-break SD different from 1, 2-breaks, Peterson
and Easterling, and Vincent, Differences in scale between two groups Siegel–Tukey,
Create reference time series mean, weights/correlation, finding outliers Grubbs,
ESD, MAD, Tietjen Moore, Hampel, etc.

Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response.