A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a prediction interval). This package has functions that calculate
     the prediction interval for the correlation (i.e., r),
     standardized mean difference (i.e., d-value), and mean.

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>.