Arc Lengths of Statistical Functions (alR)
Estimation, regression and classification using arc lengths.

Simulation Based Inference of Lasso Estimator (EAlasso)
Simulation based inference of lasso estimator. It provides several methods to sample lasso estimator: (a) Gaussian and wild multiplier bootstrap for lasso, group lasso, scaled lasso and scaled group lasso, (b) importance sampler for lasso, group lasso, scaled lasso and scaled group lasso, (c) Markov chain Monte Carlo sampler for lasso, (d) post-selection inference for lasso. See Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309> for details.

Non-Uniform Memory Access (‘NUMA’) Optimized, Parallel K-Means (knor)
The k-means ‘NUMA’ Optimized Routine library or ‘knor’ is a highly optimized and fast library for computing k-means in parallel with accelerations for Non-Uniform Memory Access (‘NUMA’) architectures.

Seeded Canonical Correlation Analysis (seedCCA)
Functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) <DOI:10.1002/cem.2691>. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) <DOI:10.1093/biomet/asm038> and Lee and Yoo (2014) <DOI:10.1111/anzs.12057>. Then, the canonical correlation is finalized with the initially-reduced two sets of variables.

Testing Differences Between Competing Risks Models and Their Visualisations (cr17)
Tool for analyzing competing risks models. The main point of interest is testing differences between groups (as described in R.J Gray (1988) <doi:10.1214/aos/1176350951> and J.P. Fine, R.J Gray (1999) <doi:10.2307/2670170>) and visualizations of survival and cumulative incidence curves.

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