Browse Items (8333 total)

Endogeneity is a common problem in any causal analysis. It arises when the independence assumption between an explanatory variable and the error in a statistical model is
violated. The causes of endogeneity are manifold and include response bias in…

Covariate-adaptive randomization is gaining popularity in clinical trials because they
enable the generation of balanced allocations with respect to covariates. Over the past
decade, substantial progress has been made in both new innovative…

The growth of populations is of interest in a broad variety of fields, such as epidemiology, economics or biology. Although a large variety of growth models are available in
the scientific literature, their application usually requires advanced…

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…

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…

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…

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 bayesTFR package for R provides a set of functions to produce probabilistic projections of the total fertility rates for all countries, and is widely used, including as part
of the basis for the United Nations official population projections for…

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…

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,…