**Quantile Regression Outlier Diagnostics with K Left Out Analysis** (**quokar**)

Diagnostics methods for quantile regression models for detecting influential observations: robust distance methods for general quantile regression models; generalized Cook’s distance and Q-function distance method for quantile regression models using aymmetric Laplace distribution. Reference of this method can be found in Luis E. Benites, Víctor H. Lachos, Filidor E. Vilca (2015) <arXiv:1509.05099v1>; mean posterior probability and Kullback-Leibler divergence methods for Bayes quantile regression model. Reference of this method is Bruno Santos, Heleno Bolfarine (2016) <arXiv:1601.07344v1>.

**Faster Computation of Common Statistics and Miscellaneous Functions** (**dvmisc**)

Faster versions of base R functions (e.g. mean, standard deviation, covariance, weighted mean), mostly written in C++, along with miscellaneous functions for various purposes (e.g. create histogram with fitted probability density function or probability mass function curve, create body mass index groups, assess linearity assumption in logistic regression).

**Tools for Combinatorics and Computational Mathematics** (**RcppAlgos**)

Provides optimized functions implemented in C++ with ‘Rcpp’. There is a generalized combinations function that is highly efficient (both speed and memory). There are optional contraint arguments that when employed, generate all combinations of a vector meeting a specific criteria (E.g. finding all combinations such that the sum is less than a bound). Additionally, there are various sieving functions that quickly generate essential components for problems common in computational mathematics (E.g. number of comprime elements, divisors, prime factorizations, and complete factorizations for many numbers as well as generating primes in a range).

**R-Friendly Threading in C++** (**RcppThread**)

Provides a C++11-style thread class and thread pool that can safely be interrupted from R.

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