Allow numbers to be presented in an English language
        version, one, two, three, ...  Ordinals are also available,
        first, second, third, ... and indefinite article choice, "a" or "an".

Computing economic analysis in civil infrastructure and  
    ecosystem restoration projects is a typical activity. This package contains Standard 
    cost engineering and engineering economics methods that are applied to convert between 
    present, future, and annualized costs. Newnan D. (2020) <ISBN 9780190931919> “Engineering Economic Analysis”.

Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). 
 <doi:10.1093/jrsssb/qkae108>.

Datasets from Nelson, Coffin and Copeland "Introductory
        Statistics for Engineering Experimentation" (Elsevier, 2003)
        with sample code.

Special functions that enhance other mixed effect model packages by creating overlayed, reduced rank, and reduced model matrices together with multiple data sets to practice the use of these models. For more details see Covarrubias-Pazaran (2016) <doi:10.1371/journal.pone.0156744>.

Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.