Browse Items (12 total)

This paper introduces the R package FKSUM, which offers fast and exact evaluation of
univariate kernel smoothers. The main kernel computations are implemented in C++, and
are wrapped in simple, intuitive and versatile R functions. The fast kernel…

This paper presents the tidypaleo package for R, which enables high-quality reproducible visualizations of time-stratigraphic multivariate data that is common to several
disciplines of the natural sciences. Rather than introduce new plotting…

This paper introduces the usage and performance of the R package tlrmvnmvt, aimed
at computing high-dimensional multivariate normal and Student-t probabilities. The
package implements the tile-low-rank methods with block reordering and the…

Linear transformation models, including the proportional hazards model and proportional odds model, under right censoring were discussed by Chen, Jin, and Ying (2002).
The asymptotic variance of the estimator they proposed has a closed form and can…

Non-regression-based inferences, such as discriminant analysis, can account for the
effect of predictor distributions that may be significant in big data modeling. We describe bbl, an R package for Boltzmann Bayes learning, which enables a…

Density estimation and inference methods are widely used in empirical work. When
the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their…

The poolr package provides an implementation of a variety of methods for pooling
(i.e., combining) p values, including Fisher’s method, Stouffer’s method, the inverse chisquare method, the binomial test, the Bonferroni method, and Tippett’s method.…

The stochastic block model is a popular probabilistic model for random graphs. It is
commonly used to cluster network data by aggregating nodes that share similar connectivity patterns into blocks. When fitting a stochastic block model to a…

Like many groups considering the new programming language Julia, we faced the
challenge of accessing the algorithms that we develop in Julia from R. Therefore, we
developed the R package JuliaConnectoR, available from the Comprehensive R…

We describe a new algorithm and R package for peak detection in genomic data sets
using constrained changepoint models. These detect changes from background to peak
regions by imposing the constraint that the mean should alternately increase then…