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.

Checks function arguments, ideally for use in R packages. Uses a simple interface and produces clean, informative error messages using 'cli'.

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>.

Provides functions to retrieve information from Web Feature Service (WFS) and Web Map Service (WMS) layers
    from various Argentine organizations and import them into R for further analysis.
    WFS and WMS are standardized protocols for serving georeferenced map data over the internet.
    For more information on these services, see <https://www.ogc.org/publications/standard/wfs/> and <https://www.ogc.org/publications/standard/wms/>.

Provides methods to construct frequentist confidence sets with valid marginal 
    coverage for identifying the population-level argmin or argmax based on IID data. 
    For instance, given an n by p loss matrix—where n is the sample size and p is the 
    number of models—the CS.argmin() method produces a discrete confidence set that contains 
    the model with the minimal (best) expected risk with desired probability. The argmin.HT() 
    method helps check if a specific model should be included in such a confidence set. The main
    implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024) 
    "Winners with confidence: Discrete argmin inference with an application to model selection".