Researchers would often like to leverage data from a collection of sources (e.g., meta-analyses of randomized trials, multi-center trials, pooled analyses of observational cohorts) to estimate causal effects in a target population of interest. However, because different data sources typically represent different underlying populations, traditional meta-analytic methods may not produce causally interpretable estimates that apply to any reasonable target population. In this article, we present the CausalMetaR R package, which implements robust and efficient methods to estimate causal effects in a given internal or external target population using multi-source data. The package includes estimators of average and subgroup treatment effects for the entire target population. To produce efficient and robust estimates of causal effects, the package implements doubly robust and non-parametric efficient estimators and supports using flexible data-adaptive (e.g., machine learning techniques) methods and cross-fitting techniques to estimate the nuisance models (e.g., the treatment model, the outcome model). We briefly review the methods, describe the key features of the package, and demonstrate how to use the package through an example. The package aims to facilitate causal analyses in the context of meta-analysis.

The use of programming languages in archaeological research has witnessed a notable surge in the last decade, particularly with R, a versatile statistical computing language that fosters the development of specialized packages. This article introduces the tesselle project (https://www.tesselle.org/), a comprehensive collection of R packages tailored for archaeological research and education. The tesselle packages are centered on quantitative analysis methods specifically crafted for archaeology. They are designed to complement both general-purpose and other specialized statistical packages. These packages serve as a versatile toolbox, facilitating the exploration and analysis of common data types in archaeology—such as count data, compositional data, or chronological data—and enabling the construction of reproducible workflows. Complementary packages for visualization, data preparation, and educational resources augment the tesselle ecosystem. This article outlines the project's inception, its objectives, design principles, and key components, along with reflections on future directions.

This article introduces datplot, an R package designed to prepare chronological data for visualization, focusing on the treatment of objects dated to overlapping periods of time. Datplot is suitable for all disciplines in which scientists long for a synoptic method that enables the visualization of the chronology of a collection of heterogeneously dated objects. It is especially helpful for visualizing trends in object assemblages over long periods of time—for example, the development of pottery styles—and it can also assist in the dating of stratigraphy. As both authors come from the field of classical archaeology, the examples and case study demonstrating the functionality of the package analyze classical materials. In particular, we focus on presenting an assemblage of epigraphic evidence from Bithynia (northwestern Turkey), with a microregional focal point in the territory of Nicaea (modern Iznik). In the article, we present the internal methodology of datplot and the process of preparing a dataset of categorically and numerically dated objects. We demonstrate visualizing the data prepared by datplot using kernel density estimation and compare the outcome with more established methods such as histograms and line graphs.