Conditionally Unbiased Bounded Influence Estimates (robcbi)
Conditionally unbiased bounded influence estimates as described in Kuensch et al. (1989) <doi:10.1080/01621459.1989.10478791> in three special cases of the generalized linear model: Bernoulli, Binomial, and Poisson distributed responses.

Adaptive Designs for Two-Stage Phase II Studies (ph2hetero)
Implementation of Jones (2007) <doi:10.1016/j.cct.2007.02.008> , Tournoux-Facon (2011) <doi:10.1002/sim.4148> and Parashar (2016) <doi:10.1002/pst.1742> designs.

Fast Spline Function Based Constrained Maximum Likelihood Estimator for AUC Estimation of Interval Censored Survival Data (intcensROC)
The kernel of this ‘Rcpp’ based package is an efficient implementation of the generalized gradient projection method for spline function based constrained maximum likelihood estimator for interval censored survival data (Wu, Yuan; Zhang, Ying. Partially monotone tensor spline estimation of the joint distribution function with bivariate current status data. Ann. Statist. 40, 2012, 1609-1636 <doi:10.1214/12-AOS1016>). The key function computes the density function of the joint distribution of event time and the marker and returns the receiver operating characteristic (ROC) curve for the interval censored survival data as well as area under the curve (AUC).

Providing Interactive Interpretations and Explanations of Statistical Results (xplain)
Allows to provide live interpretations and explanations of statistical functions in R. These interpretations and explanations are shown when the explained function is called by the user. They can interact with the values of the explained function’s actual results to offer relevant, meaningful insights. The interpretations and explanations are based on an easy-to-use XML format that allows to include R code to interact with the returns of the explained function.

Local Group Graphical Lasso Estimation (loggle)
Provides a set of methods that learn time-varying graphical models based on data measured over a temporal grid. The underlying statistical model is motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance the study of how stocks interact with each other and how such interactions change over time. The time-varying graphical models are estimated under the assumption that the graph topology changes gradually over time. For more details on estimating time-varying graphical models, please refer to: Yang, J. & Peng, J. (2018) <arXiv:1804.03811>.