Weighted Quantile Sum Regression (wqs)
Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights.
Statistical Functions for the Censored and Uncensored Epanechnikov Distribution (epandist)
Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
General Semiparametric Shared Frailty Model (frailtySurv)
Simulates and fits semiparametric shared frailty models under a wide range of frailty distributions using a consistent and asymptotically-normal estimator. Currently supports: gamma, power variance function, log-normal, and inverse Gaussian frailty models.
Global Optimization by Differential Evolution in C++ (RcppDE)
An efficient C++ based implementation of the ‘DEoptim’ function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation ‘easier, shorter, faster: pick any two’ could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in ‘DEoptim’.
Assertions to Check Properties of Sets (assertive.sets)
A set of predicates and assertions for checking the properties of sets. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly.