Adversarial random forests (ARFs) recursively partition data into 
    fully factorized leaves, where features are jointly independent. The 
    procedure is iterative, with alternating rounds of generation and 
    discrimination. Data becomes increasingly realistic at each round, until 
    original and synthetic samples can no longer be reliably distinguished. 
    This is useful for several unsupervised learning tasks, such as density
    estimation and data synthesis. Methods for both are implemented in this
    package. ARFs naturally handle unstructured data with mixed continuous and 
    categorical covariates. They inherit many of the benefits of random forests, 
    including speed, flexibility, and solid performance with default parameters. 
    For details, see Watson et al. (2023) <https://proceedings.mlr.press/v206/watson23a.html>.

Simulates, fits, and predicts long-memory and anti-persistent
	time series, possibly mixed with ARMA, regression, transfer-function
	components.
	Exact methods (MLE, forecasting, simulation) are used.
	Bug reports should be done via GitHub (at
	<https://github.com/JQVeenstra/arfima>), where the development version
	of this package lives; it can be installed using devtools.

Provides functions to access data from public RESTful APIs including the 'ArgentinaDatos API', 
    'REST Countries API', and 'World Bank API' related to Argentina's exchange rates, inflation, 
    political figures, holidays, economic indicators, and general country-level statistics. 
    Additionally, the package includes curated datasets related to Argentina, covering topics 
    such as economic indicators, biodiversity, agriculture, human rights, genetic data, and 
    consumer prices. The package supports research and analysis focused on Argentina by 
    integrating open APIs with high-quality datasets from various domains. 
    For more details on the APIs, see: 
        'ArgentinaDatos API' <https://argentinadatos.com/>, 
        'REST Countries API' <https://restcountries.com/>, 
        and 'World Bank API' <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>.