Functions to get prediction intervals and prediction points of future observations from any continuous distribution.

Enables researchers to visualize the prediction performance of any algorithm on the individual level (or close to it), given that the predicted outcome is either binary or continuous. Visual results are instantly comprehensible.

Providing functions to diagnose and make inferences from various linear models, 
    such as those obtained from 'aov', 'lm', 'glm', 'gls', 'lme', 'lmer', 'glmmTMB' and 'semireg'. 
	Inferences include predicted means and standard errors, contrasts, multiple comparisons, 
	permutation tests, adjusted R-square and graphs.

Estimates when and where a model-guided treatment strategy may 
    outperform a treat-all or treat-none approach by Monte Carlo simulation and 
    evaluation of the Net Monetary Benefit. Details can be viewed in 
    Parsons et al. (2023) <doi:10.21105/joss.05328>.

Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.

Consider a linear predictive regression setting with a potentially large set of candidate predictors. This work is concerned with detecting the presence of out of sample predictability based on out of sample mean squared error comparisons given in Gonzalo and Pitarakis (2023) <doi:10.1016/j.ijforecast.2023.10.005>.

Predicts the most common race of a surname and based on U.S. Census
    data, and the most common first named based on U.S. Social Security Administration data.

Methods for spatial predictive modeling, especially for spatial distribution models. This includes algorithms for model fitting and prediction, as well as methods for model evaluation.

Fetches the 'PREDICTS' database and relevant metadata from the Data
    Portal at the Natural History Museum, London <https://data.nhm.ac.uk>. Data
    were collated from over 400 existing spatial comparisons of local-scale
    biodiversity exposed to different intensities and types of anthropogenic
    pressures, from sites around the world. These data are described in Hudson
    et al. (2013) <doi:10.1002/ece3.2579>.