Image analysis techniques provide objective and reproducible statistics for interpreting microscopy data. At higher dimensions, three-dimensional (3D) volumetric and spatiotemporal data highlight additional properties and behaviors beyond the static 2D focal plane. However, increased dimensionality carries increased complexity, and existing techniques for general segmentation of 3D data are either primitive, or highly specialized to specific biological structures. Borrowing from the principles of 2D topological data analysis (TDA), we formulate a 3D segmentation algorithm that implements persistent homology to identify variations in image intensity. From this, we derive two separate variants applicable to spatial and spatiotemporal data, respectively. We demonstrate that this analysis yields both sensitive and specific results on simulated data and can distinguish prominent biological structures in fluorescence microscopy images, regardless of their shape. Furthermore, we highlight the efficacy of temporal TDA in tracking cell lineage and the frequency of cell and organelle replication.

For much of its history, archaeological research has relied on site-specific projects, regional comparisons, and theory building from case studies. However, recent research themes concerning the emergence of complex social-ecological systems and long-term land-use legacies require a new approach to archaeological data. Large-scale syntheses of archaeological data provide an effective way forward to address these new research themes. In more concise terms, “big questions” require “big data” to help answer them. The archaeological information collected by the USDA Forest Service is one such “big dataset” and represents an incalculable investment in time, resources, and expertise. This article explores this concept and presents an R package (ArchaeoSRP) designed to extract archaeological information from USDA Forest Service site record files. We demonstrate the functionality of this R package through a case study examining the archaeological data for the Cle Elum Ranger District, within Central Washington's Okanogan-Wenatchee National Forest. Our results reveal the efficiency of using automated methods to extract, organize, and synthesize district-level archaeological data, which, in turn, reveal patterns of precontact and historic land use that were otherwise not distinguishable.

This work presents a $\textsf{Python}$ EMD package named AdvEMDpy that is both more flexible and generalises existing empirical mode decomposition (EMD) packages in $\textsf{Python}$, $\textsf{R}$, and $\textsf{MATLAB}$. It is aimed specifically for use by the insurance and financial risk communities, for applications such as return modelling, claims modelling, and life insurance applications with a particular focus on mortality modelling. AdvEMDpy both expands upon the EMD options and methods available, and improves their statistical robustness and efficiency, providing a robust, usable, and reliable toolbox. Unlike many EMD packages, AdvEMDpy allows customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analyses can be performed. The intrinsic mode functions (IMFs) extracted using EMD contain complex multi-frequency structures which warrant maximum algorithmic customisation for effective analysis. A major contribution of this package is the intensive treatment of the EMD edge effect which is the most ubiquitous problem in EMD and time series analysis. Various EMD techniques, of varying intricacy from numerous works, have been developed, refined, and, for the first time, compiled in AdvEMDpy. In addition to the EMD edge effect, numerous pre-processing, post-processing, detrended fluctuation analysis (localised trend estimation) techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and other algorithmic variations have been incorporated and presented to the users of AdvEMDpy. This paper and the supplementary materials provide several real-world actuarial applications of this package for the user’s benefit.

True and Error Theory (TET) provides a method to separate the variability of behavior into components due to changing true policy and to random error. TET is a testable theory that can serve as a statistical model, allowing one to evaluate substantive theories as nested, special cases. TET is more accurate descriptively and has theoretical advantages over previous approaches. This paper presents a freely available computer program in R that can be used to fit and evaluate both TET and substantive theories that are special cases of it. The program performs Monte Carlo simulations to generate distributions of test statistics and bootstrapping to provide confidence intervals on parameter estimates. Use of the program is illustrated by a reanalysis of previously published data testing whether what appeared to be violations of Expected Utility (EU) theory (Allais paradoxes) by previous methods might actually be consistent with EU theory.

One major challenge to behavioral decision research is to identify the cognitive processes underlying judgment and decision making. Glöckner (2009) has argued that, compared to previous methods, process models can be more efficiently tested by simultaneously analyzing choices, decision times, and confidence judgments. The Multiple-Measure Maximum Likelihood (MM-ML) strategy classification method was developed for this purpose and implemented as a ready-to-use routine in STATA, a commercial package for statistical data analysis. In the present article, we describe the implementation of MM-ML in R, a free package for data analysis under the GNU general public license, and we provide a practical guide to application. We also provide MM-ML as an easy-to-use R function. Thus, prior knowledge of R programming is not necessary for those interested in using MM-ML.

Numerical estimators of differential entropy and mutual information can be slow to converge as sample size increases. The offset Kozachenko–Leonenko (KLo) method described here implements an offset version of the Kozachenko–Leonenko estimator that can markedly improve convergence. Its use is illustrated in applications to the comparison of trivariate data from successive scene color images and the comparison of univariate data from stereophonic music tracks. Publicly available code for KLo estimation of both differential entropy and mutual information is provided for R, Python, and MATLAB computing environments at https://github.com/imarinfr/klo.

The documentation and analysis of archaeological lithics must navigate a basic tension between examining and recording data on individual artifacts or on aggregates of artifacts. This poses a challenge both for artifact processing and for database construction. We present here an R Shiny solution that enables lithic analysts to enter data for both individual artifacts and aggregates of artifacts while maintaining a robust yet flexible data structure. This takes the form of a browser-based database interface that uses R to query existing data and transform new data as necessary so that users entering data of varying resolutions still produce data structured around individual artifacts. We demonstrate the function and efficacy of this tool (termed the Queryable Artifact Recording Interface [QuARI]) using the example of the Stelida Naxos Archaeological Project (SNAP), which, focused on a Paleolithic and Mesolithic chert quarry, has necessarily confronted challenges of processing and analyzing large quantities of lithic material.

Knowledge about political representatives' behavior is crucial for a deeper understanding of politics and policy-making processes. Yet resources on legislative elites are scattered, often specialized, limited in scope or not always accessible. This article introduces the Comparative Legislators Database (CLD), which joins micro-data collection efforts on open-collaboration platforms and other sources, and integrates with renowned political science datasets. The CLD includes political, sociodemographic, career, online presence, public attention, and visual information for over 45,000 contemporary and historical politicians from ten countries. The authors provide a straightforward and open-source interface to the database through an R package, offering targeted, fast and analysis-ready access in formats familiar to social scientists and standardized across time and space. The data is verified against human-coded datasets, and its use for investigating legislator prominence and turnover is illustrated. The CLD contributes to a central hub for versatile information about legislators and their behavior, supporting individual-level comparative research over long periods.

Growth is one of the most studied plant responses. At the cellular level, plant growth is driven by cell division and cell expansion. A means to quantify these two cellular processes is through kinematic analysis, a methodology that has been developed and perfected over the past decades, with in-depth descriptions of the methodology available. Unfortunately, after performing the lab work, researchers are required to perform time-consuming, repetitive and error-prone calculations. To lower the barrier towards this final step in the analysis and to aid researchers currently applying this technique, we have created leafkin, an R-package to perform all the calculations involved in the kinematic analysis of monocot leaves using only four functions. These functions support leaf elongation rate calculations, fitting of cell length profiles, extraction of fitted cell lengths and execution of kinematic equations. With the leafkin package, kinematic analysis of monocot leaves becomes more accessible than before.

Structural equation modeling (SEM) is an important research tool, both for path-based model specification (common in the social sciences) and also for matrix-based models (in heavy use in behavior genetics). We developed umx to give more immediate access, relatively concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification and comparison of models, as well as both graphical and tabular outputs. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multigroup twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models, including support for covariates, common- and independent-pathway models, and gene × environment interaction models. A tutorial site and question forum are also available.