A revision to the stats::ks.test() function and the associated
        ks.test.Rd help page. With one minor exception, it does not change the
        existing behavior of ks.test(), and it adds features necessary
        for doing one-sample tests with hypothesized discrete
        distributions. The package also contains cvm.test(), for doing
        one-sample Cramer-von Mises goodness-of-fit tests.

Interface to the 'python' package 'dgpsi' for Gaussian process, deep Gaussian process, 
             and linked deep Gaussian process emulations of computer models and networks using stochastic imputation (SI). 
             The implementations follow Ming & Guillas (2021) <doi:10.1137/20M1323771> and 
             Ming, Williamson, & Guillas (2023) <doi:10.1080/00401706.2022.2124311> and 
             Ming & Williamson (2023) <arXiv:2306.01212>. To get started with the package, 
             see <https://mingdeyu.github.io/dgpsi-R/>.

Gumbel distribution functions (De Haan L. (2007)
    <doi:10.1007/0-387-34471-3>) implemented with the techniques of automatic
    differentiation (Griewank A. (2008) <isbn:978-0-89871-659-7>).
    With this tool, a user should be able to quickly model extreme
    events for which the Gumbel distribution is the domain of attraction.
    The package makes available the density function, the distribution
    function the quantile function and a random generating function. In
    addition, it supports gradient functions. The package combines 'Adept'
    (C++ templated automatic differentiation) (Hogan R. (2017)
    <doi:10.5281/zenodo.1004730>) and 'Eigen' (templated matrix-vector
    library) for fast computations of both objective functions and exact
    gradients. It relies on 'RcppEigen' for easy access to 'Eigen' and
    bindings to R.

The 'DHARMa' package uses a simulation-based approach to create
    readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed
    models. Currently supported are linear and generalized linear (mixed) models from 'lme4'
    (classes 'lmerMod', 'glmerMod'), 'glmmTMB' 'GLMMadaptive' and 'spaMM', generalized additive models
    ('gam' from 'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and
    'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations
    from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well.
    The resulting residuals are standardized to values between 0 and 1 and can be interpreted
    as intuitively as residuals from a linear regression. The package also provides a number of
    plot and test functions for typical model misspecification problems, such as
    over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.