Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data applying customized linear scan algorithms, proposed by Li and colleagues (2022) <doi:10.1155/2022/1362913>. 
             The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal 
             outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model 
             is estimated using an Expectation Maximization algorithm.

This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time.

A computationally efficient and statistically rigorous fast 
    Kernel Machine method for multi-kernel analysis. The approach is based on 
    a low-rank approximation to the nuisance effect kernel matrices. The 
    algorithm is applicable to continuous, binary, and survival traits and 
    is implemented using the existing single-kernel analysis software 'SKAT' 
    and 'coxKM'. 'coxKM' can be obtained from 
    <https://github.com/lin-lab/coxKM>.

R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert, Peter J. Rousseeuw 2019 <doi:10.1007/978-3-030-32047-8_16>.

Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time.

An efficient algorithm to fit and tune kernel quantile regression models based on the majorization-minimization (MM) method. It can also fit multiple quantile curves simultaneously without crossing.