Enhancing cross-language compatibility within the 'RStudio' environment and supporting seamless language understanding, 
    the 'deepRstudio' package leverages the power of the 'DeepL' API (see <https://www.deepl.com/docs-api>) to enable seamless, fast, 
    accurate, and affordable translation of code comments, documents, and text. This package offers the ability to translate selected text 
    into English (EN), as well as from English into various languages, namely Japanese (JA), Chinese (ZH), Spanish (ES), 
    French (FR), Russian (RU), Portuguese (PT), and Indonesian (ID). With much of the text being written in English, the emphasis 
    is on compatibility from English. It is also designed for developers working on multilingual projects and data analysts collaborating 
    with international teams, simplifying the translation process and making code more accessible and comprehensible to people with 
    diverse language backgrounds. This package uses the 'rstudioapi' package and 'DeepL' API, and is simply implemented, executed 
    from addins or via shortcuts on 'RStudio'. With just a few steps, content can be translated between supported languages, promoting better 
    collaboration and expanding the global reach of work. The functionality of this package works only on 'RStudio' using 'rstudioapi'.

Deep compositional spatial models are standard spatial covariance
             models coupled with an injective warping function of the spatial 
             domain. The warping function is constructed through a composition 
             of multiple elemental injective functions in a deep-learning 
             framework. The package implements two cases for the univariate setting; first,
	     when these warping functions are known up to some weights that
	     need to be estimated, and, second, when the weights in each layer are random.
	     In the multivariate setting only the former case is available.
	     Estimation and inference is done using `tensorflow`, which makes use of 
             graphics processing units. 
             For more details see Zammit-Mangion et al. (2022) <doi:10.1080/01621459.2021.1887741>,
             Vu et al. (2022) <doi:10.5705/ss.202020.0156>,
             Vu et al. (2023) <doi:10.1016/j.spasta.2023.100742>, and
             Shao et al. (2025) <doi:10.48550/arXiv.2505.12548>.