This web client interfaces Unpaywall <https://unpaywall.org/products/api>, formerly
    oaDOI, a service finding free full-texts of academic papers by linking DOIs with 
    open access journals and repositories. It provides unified access to various data sources 
    for open access full-text links including Crossref and the Directory of Open Access 
    Journals (DOAJ). API usage is free and no registration is required.

Iterative least cost path and minimum spanning tree methods for projecting 
    forest road networks. The methods connect a set of target points to an existing 
    road network using 'igraph' <https://igraph.org> to identify least cost routes.
    The cost of constructing a road segment between adjacent pixels is determined
    by a user supplied weight raster and a weight function; options include the
    average of adjacent weight raster values, and a function of the elevation 
    differences between adjacent cells that penalizes steep grades. These road
    network projection methods are intended for integration into R workflows and 
    modelling frameworks used for forecasting forest change, and can be applied 
    over multiple time-steps without rebuilding a graph at each time-step.

A collection of methods for the robust analysis of univariate and
    multivariate functional data, possibly in high-dimensional cases, and hence
    with attention to computational efficiency and simplicity of use. See the R 
    Journal publication of Ieva et al. (2019) <doi:10.32614/RJ-2019-032> for an 
    in-depth presentation of the 'roahd' package. See Aleman-Gomez et al. (2021) 
    <arXiv:2103.08874> for details about the concept of depthgram.

Provide helper functions for package developers to create 
    active bindings that looks like data embedded in the package, but 
    are downloaded from remote sources.

Provides an interface to the OAuth 1.0 specification
        allowing users to authenticate via OAuth to the
        server of their choice.

It enables the identification of sequentialexperimentation orders for factorial designs that jointly reduce bias and the number of level changes. The method used is that presented by Conto et al. (2025), known as the Assignment-Expansion method, which consists of adapting the linear programming assignment problem to generate balanced experimentation orders. The properties identified are then generalized to designs with a larger number of factors and levels using the expansion method proposed by Correa et al. (2009) and later generalized by Bhowmik et al. (2017). For more details see Conto et al. (2025) <doi:10.1016/j.cie.2024.110844>, Correa et al. (2009) <doi:10.1080/02664760802499337> and Bhowmik et al. (2017) <doi:10.1080/03610926.2016.1152490>.