A unified framework for unit root and stationarity testing
    including quantile ADF tests (Koenker and Xiao, 2004)
    <doi:10.1198/016214504000001114>, GARCH-based unit root tests with
    endogenous structural breaks (Narayan and Liu, 2015)
    <doi:10.1016/j.eneco.2014.11.021>, and comprehensive Dickey-Fuller,
    Phillips-Perron, KPSS, ERS/DF-GLS, Zivot-Andrews, and
    Kobayashi-McAleer tests with an Elder-Kennedy decision strategy
    (Elder and Kennedy, 2001) <doi:10.1080/00220480109595179>.

Support for measurement units in R vectors, matrices
    and arrays: automatic propagation, conversion, derivation
    and simplification of units; raising errors in case of unit
    incompatibility. Compatible with the POSIXct, Date and difftime 
    classes. Uses the UNIDATA udunits library and unit database for 
    unit compatibility checking and conversion.
    Documentation about 'units' is provided in the paper by Pebesma, Mailund &
    Hiebert (2016, <doi:10.32614/RJ-2016-061>), included in this package as a
    vignette; see 'citation("units")' for details.

A test to understand the stability of the underlying 
    stochastic data. Helps the user’s understand whether the random variable under 
    consideration is stationary or non-stationary without any manual interpretation 
    of the results. It further ensures to check all the prerequisites and assumptions which 
    are underlying the unit root test statistics and if the underlying data is found to be non-stationary in all
    the 4 lags the function diagnoses the input data and returns with an optimised solution on the same.

Concise TAP <http://testanything.org/> compliant unit testing package. Authored tests can be run using CMD check with minimal implementation overhead.

Implementation of the unity forest (UFO) framework (Hornung & Hapfelmeier, 2026,
  <doi:10.48550/arXiv.2601.07003>).
  UFOs are a random forest variant designed to better take covariates with purely
  interaction-based effects into account, including interactions for which none of the involved
  covariates exhibits a marginal effect.
  While this framework tends to improve discrimination and predictive accuracy compared
  to standard random forests, it also facilitates the identification and interpretation of
  (marginal or interactive) effects: In addition to the UFO algorithm for tree construction,
  the package includes the unity variable importance measure (unity VIM), which quantifies
  covariate effects under the conditions in which they are strongest - either marginally 
  or within subgroups defined by interactions - as well as covariate-representative tree
  roots (CRTRs) that provide interpretable visualizations of these conditions.
  Categorical and continuous outcomes are supported.
  This package is a fork of the R package 'ranger' (main author: Marvin N. Wright), which
  implements random forests using an efficient C++ backend.