Miscellaneous Functions (miscF)
Various functions for random number generation, density estimation, classification, curve fitting, and spatial data analysis.

Random Graph Clustering (mixer)
Estimates the parameters, the clusters, as well as the number of clusters of a (binary) stochastic block model (J.-J Daudin, F. Picard, S. Robin (2008) <doi:10.1007/s11222-007-9046-7>).

Simulate Dynamic Networks using Exponential Random Graph Models (ERGM) Family (dnr)
Functions are provided to fit temporal lag models to dynamic networks. The models are build on top of exponential random graph models (ERGM) framework. There are functions for simulating or forecasting networks for future time points. Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models. Mallik, Almquist (2017, under review).

Analysis of Repeatability and Reproducibility Studies with Ordinal Measurements (ordinalRR)
Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with ‘rjags’. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper.

Maximizing the Adjusted AUC (maxadjAUC)
Fits a linear combination of predictors by maximizing a smooth approximation to the estimated covariate-adjusted area under the receiver operating characteristic curve (AUC) for a discrete covariate. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://…/>.)