Browse Items (12 total)

Quantile-quantile (Q-Q) plots are often difficult to interpret because it is unclear
how large the deviation from the theoretical distribution must be to indicate a lack of
fit. Most Q-Q plots could benefit from the addition of meaningful global…

Unit root tests form an essential part of any time series analysis. We provide practitioners with a single, unified framework for comprehensive and reliable unit root testing
in the R package bootUR. The package’s backbone is the popular augmented…

Many longitudinal studies collect data that have irregular observation times, often
requiring the application of linear mixed models with time-varying outcomes. This paper
presents an alternative that splits the quantitative analysis into two…

Disaggregation modeling, or downscaling, has become an important discipline in epidemiology. Surveillance data, aggregated over large regions, is becoming more common,
leading to an increasing demand for modeling frameworks that can deal with this…

Analysis of dose-response data is an important step in many scientific disciplines,
including but not limited to pharmacology, toxicology, and epidemiology. The R package
drda is designed to facilitate the analysis of dose-response data by…

The lasso and elastic net are popular regularized regression models for supervised
learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient
algorithm for computing the elastic net regularization path for ordinary…

In a world with data that change rapidly and abruptly, it is important to detect those
changes accurately. In this paper we describe an R package implementing a generalized
version of an algorithm recently proposed by Hocking, Rigaill, Fearnhead,…

This article illustrates intRinsic, an R package that implements novel state-of-the-art
likelihood-based estimators of the intrinsic dimension of a dataset, an essential quantity
for most dimensionality reduction techniques. In order to make these…

The R package MLGL, standing for multi-layer group-Lasso, implements a new procedure of variable selection in the context of redundancy between explanatory variables,
which holds true with high-dimensional data. A sparsity assumption is made that…

Network meta-analysis compares different interventions for the same condition, by
combining direct and indirect evidence derived from all eligible studies. Network metaanalysis has been increasingly used by applied scientists and it is a major…