Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. Empirical likelihood tests within a Cox model, for parameters defined via both baseline hazard function and regression parameters.

This package implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.

Allows for easy creation of diagnostic plots for a variety of model objects using the Grammar of Graphics. Provides functionality for both individual diagnostic plots and an array of four standard diagnostic plots.

Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge.

Hadoop InteractiVE facilitates distributed computing via the MapReduce paradigm through R and Hadoop. An easy to use interface to Hadoop, the Hadoop Distributed File System (HDFS), and Hadoop Streaming is provided.

We provide data sets used in the forthcoming textbook "Introduction to Sports Analytics using R" by Elmore and Urbaczweski (2024). The package currently contains sixteen datasets and should be published in early 2024.

This package provides functions and data to reproduce all plots in the book "Practical Smoothing. The Joys of P-splines" by Paul H.C. Eilers and Brian D. Marx (2021, ISBN:978-1108482950).

This package implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively. The method is documented in Kumar et al (2013) <https://www.nature.com/articles/jes201352)>.

Multiscale moving sum procedure for the detection of changes in expectation in univariate sequences. References - Multiscale change point detection via gradual bandwidth adjustment in moving sum processes (2021+), Tijana Levajkovic and Michael Messer.

It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).

Interfaces the Python library zuko implementing Masked Autoregressive Flows. See Rozet, Divo and Schnake (2023) <doi:10.5281/zenodo.7625672> and Papamakarios, Pavlakou and Murray (2017) <doi:10.48550/arXiv.1705.07057>.

This package provides functions and datasets used in the book: Fernandez-Casal, R., Costa, J. and Oviedo-de la Fuente, M. (2024) "Metodos predictivos de aprendizaje estadistico" <https://rubenfcasal.github.io/aprendizaje_estadistico/>.

Allows users to produce estimates and MSE for multivariate variables using Linear Mixed Model. The package follows the approach of Datta, Day and Basawa (1999) <doi:10.1016/S0378-3758(98)00147-5>.

R package associated with the Multiple Approximate Kernel Learning (MAKL) algorithm proposed in <doi:10.1093/bioinformatics/btac241>. The algorithm fits multiple approximate kernel learning (MAKL) models that are fast, scalable and interpretable.

Estimate nonlinear vector autoregression models (also known as the next generation reservoir computing) for nonlinear dynamic systems. The algorithm was described by Gauthier et al. (2021) <doi:10.1038/s41467-021-25801-2>.

This package provides a Shiny Web Application to predict and visualize concentrations of pharmaceuticals in the aqueous environment. Jagadeesan K., Barden R. and Kasprzyk-Hordern B. (2022) <https://www.ssrn.com/abstract=4306129>.

This package provides a simple implementation of the Predictive Information Index ('PII') using mutual information and entropy from the infotheo package. For related methodology, see Wells (2025) <https://github.com/TheotherDrWells/piiR>.

Design single-case phase, alternation and multiple-baseline experiments, and conduct randomization tests on data gathered by means of such designs, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.

Implementation of several recent multivariate bias correction methods with a unified interface to facilitate their use. A description and comparison between methods can be found in <doi:10.5194/esd-11-537-2020>.

Computes synchrony as windowed cross-correlation based on two-dimensional time series in a text file you can upload. SUSY works as described in Tschacher & Meier (2020) <doi:10.1080/10503307.2019.1612114>.

An interactive document on the topic of basic statistical analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://jarvisatharva.shinyapps.io/StatisticsPrimer/>.

This package provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.

Fits Dirichlet regression and zero-and-one inflated Dirichlet regression with Bayesian methods implemented in Stan. These models are sometimes referred to as trinomial mixture models; covariates and overdispersion can optionally be included.

Perform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease.