Investigating New Projection Pursuit Index Functions (spinebil)
Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. The ‘spinebil’ package contains methods to evaluate the performance of projection pursuit index functions using tour methods, as described in Laa & Cook (2019) <arXiv:1902.00181>.

Scaled Logistic Regression (sclr)
Maximum likelihood estimation of the scaled logit model parameters proposed in Dunning (2006) <doi:10.1002/sim.2282>.

Confidence Intervals for Exceedance Probability (exceedProb)
Computes confidence intervals for the exceedance probability of normally distributed estimators. Currently only supports general linear models. Please see Segal (2019) <arXiv:1803.03356> for more information.

Bayesian Analysis of Non-Stationary Gaussian Process Models (BayesNSGP)
Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <arXiv:1702.00434v2>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the ‘nimble’ package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.

A Cursory Look at Variables (cursory)
Provides functions for quickly looking at summary statistics for variables in a data set.