Uncertainty Propagation Analysis (spup)
Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2017) <doi:10.1080/13658810601063951> and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally.

An Easy Way to Report ROC Analysis (reportROC)
Provides an easy way to report the results of ROC analysis, including: 1. an ROC curve. 2. the value of Cutoff, SEN (sensitivity), SPE (specificity), AUC (Area Under Curve), AUC.SE (the standard error of AUC), PLR (positive likelihood ratio), NLR (negative likelihood ratio), PPV (positive predictive value), NPV (negative predictive value).

Estimating Finite Population Total (fpest)
Given the values of sampled units and selection probabilities the desraj function in the package computes the estimated value of the total as well as estimated variance.

Regression Analysis Based on Win Loss Endpoints (WLreg)
Use various regression models for the analysis of win loss endpoints adjusting for non-binary and multivariate covariates.

Smoothed Bootstrap and Random Generation from Kernel Densities (kernelboot)
Smoothed bootstrap and functions for random generation from univariate and multivariate kernel densities. It does not estimate kernel densities.