c(
person("Kleanthis", "Natsiopoulos",
email = "klnatsio@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-1180-2984")),
person("Nickolaos", "Tzeremes",
email = "bus9nt@econ.uth.gr", role = "aut",
comment = c(ORCID = "0000-0002-6938-3404")))
Kleanthis Natsiopoulos [aut, cre]
(<https://orcid.org/0000-0003-1180-2984>),
Nickolaos Tzeremes [aut] (<https://orcid.org/0000-0002-6938-3404>)

Creates complex autoregressive distributed lag (ARDL) models and
constructs the underlying unrestricted and restricted error correction
model (ECM) automatically, just by providing the order. It also performs
the bounds-test for cointegration as described in Pesaran et al. (2001)
<doi:10.1002/jae.616> and provides the multipliers and the cointegrating
equation. The validity and the accuracy of this package have been verified
by successfully replicating the results of Pesaran et al. (2001) in
Natsiopoulos and Tzeremes (2022) <doi:10.1002/jae.2919>.

qpcR, sandwich, testthat (>= 3.0.0), xts

Allelic richness estimation, with extrapolation beyond the sample size
Generates an allelic richness accumulation curve. This curve shows
the expected number of unique alleles in a population when taking a sample of
individuals. The function aresCalc takes a binary data matrix as input,
showing the presence of alleles per individual, and gives an accumulation
curve (mean with 95\% confidence bounds) back. The function aresPlot can be
used to plot the output from aresCalc.

Washington Junger <wjunger@ims.uerj.br> and Antonio Ponce de
Leon <ponce@ims.uerj.br>
Environment air pollution epidemiology: a library for time
series analysis
A toolbox for time series analyses using generalized
additive models. This library includes routines for model
estimation and diagnostics. Polynomial distributed lag models
are also implemented.

Adaptable Regularized Hotelling's T^2 Test for High-Dimensional
Data
person("Haoran", "Li", email = "hrli@ucdavis.edu", role = c("aut", "cre"))
Perform the Adaptable Regularized Hotelling's T^2 test (ARHT) proposed by
Li et al., (2016) <arXiv:1609.08725>. Both one-sample and two-sample mean test are available with
various probabilistic alternative prior models. It contains a function to consistently
estimate higher order moments of the population covariance spectral distribution using
the spectral of the sample covariance matrix (Bai et al. (2010) <doi:10.1111/j.1467-842X.2010.00590.x>).
In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately
with a given correlation matrix when the degrees of freedom are large.