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Open-ended assignments - such as lab reports and semester-long projects - provide data science and statistics students with opportunities for developing communication, critical thinking, and creativity skills. However, providing grades and qualitative feedback to open-ended assignments can be very time consuming and difficult to do consistently across students. In this paper, we discuss the steps of a typical grading workflow and highlight which steps can be automated in an approach that we define as an automated grading workflow. We illustrate how gradetools, a new R package, implements this approach within RStudio to facilitate efficient and consistent grading while providing individualized feedback. We hope that this work will help the community of data science and statistics educators use gradetools as their grading workflow assistant or develop their own tools for assisting their grading workflow.

This tutorial introduces the graded response model (GRM), a tool for testing measurement precision within the item response theory (IRT) paradigm, which is useful for informing researchers about the item and person properties of their measurement. The tutorial aims to guide applied researchers through a unidimensional GRM analysis in the R environment, using the psych, mirt and ggmirt packages. GRM is specifically designed to examine the psychometric properties of psychological scales with polytomous (Likert‐style) items. The tutorial illustrates the procedure using data from the Open Psychometrics Database on the right‐wing authoritarianism (RWA) scale, outlining the theoretical underpinnings of GRM and steps for data preparation, testing model assumptions, model fitting, plotting item parameters and interpretation of results. [ABSTRACT FROM AUTHOR]

The democratisation of next‐generation sequencing has vastly increased the availability of sequencing data from metabarcoding. However, to effectively prepare these metabarcoding data for subsequent analysis, researchers must consistently apply several different bioinformatic tools—including those which denoise reads, cluster sequences and assign taxonomic identities. This often creates a bioinformatics bottleneck in workflows for non‐specialists due to obstacles around: (a) integrating different tools, (b) the inability to easily modify and rerun bioinformatic pipelines involving non‐scripted ('point‐and‐click') elements and (c) the multiple outputs that may be required of a single dataset (e.g. amplicon sequence variants [ASVs] and operational taxonomic units [OTUs]), which often results in users running pipelines multiple times.Here, we introduce SimpleMetaPipeline, an open‐source bioinformatics pipeline implemented in R, which addresses these obstacles. SimpleMetaPipeline integrates the most robust and commonly used existing bioinformatic tools in a single reproducible pipeline, with a streamlined choice of parameters, to generate a sequence data table containing alternative clustering and assignment options. SimpleMetaPipeline accepts demultiplexed paired‐end and single reads from multiple sequencing runs.We describe the pipeline and demonstrate how alternative annotations enable the easy implementation of multi‐algorithm agreement tests to strengthen inferences.SimpleMetaPipeline represents a valuable addition to the existing library of pipelines, providing easy and reproducible bioinformatics, including a range of commonly desired clustering and assignment options, such as OTUs and ASVs. [ABSTRACT FROM AUTHOR]

Background: The dilution-replicate experimental design for qPCR assays is especially efficient. It is based on multiple linear regression of multiple 3-point standard curves that are derived from the experimental samples themselves and thus obviates the need for a separate standard curve produced by serial dilution of a standard. The method minimizes the total number of reactions and guarantees that Cq values are within the linear dynamic range of the dilution-replicate standard curves. However, the lack of specialized software has so far precluded the widespread use of the dilution-replicate approach. Results: Here we present repDilPCR, the first tool that utilizes the dilution-replicate method and extends it by adding the possibility to use multiple reference genes. repDilPCR offers extensive statistical and graphical functions that can also be used with preprocessed data (relative expression values) obtained by usual assay designs and evaluation methods. repDilPCR has been designed with the philosophy to automate and speed up data analysis (typically less than a minute from Cq values to publication-ready plots), and features automatic selection and performance of appropriate statistical tests, at least in the case of one-factor experimental designs. Nevertheless, the program also allows users to export intermediate data and perform more sophisticated analyses with external statistical software, e.g. if two-way ANOVA is necessary. Conclusions: repDilPCR is a user-friendly tool that can contribute to more efficient planning of qPCR experiments and their robust analysis. A public web server is freely accessible at https://repdilpcr.eu without registration. The program can also be used as an R script or as a locally installed Shiny app, which can be downloaded from https://github.com/deyanyosifov/repDilPCR where also the source code is available. [ABSTRACT FROM AUTHOR]

This article combines linguistic analysis and data mining methods to explore variations in speakers' evaluative meaning-making in conflict talks. It focuses on conflict style construction through evaluative language, specifically how disputants advance attitudes. The corpus consists of 230 minutes of family mediation talks involving 12 divorcing spouses. The research draws from the Appraisal framework to analyse evaluative meaning-making at a discourse semantics level, capturing both explicit and implicit attitudes, as well as the scaling and dialogic framing of attitudes. Data exploration uses clustering algorithms via RStudio to identify variations in disputants' discursive behaviour. The findings uncover three conflict styles based on disputants' preference for attitude advancement formulations, with varying degrees of assertiveness and forcefulness. This study's contributions include a holistic treatment of evaluative meaning-making, the marriage of digital tools to nuanced linguistic annotation, and a novel interpretation for conflict style. The findings offer fresh insights into disputants' discursive self-presentation in confrontational exchanges. [ABSTRACT FROM AUTHOR]

Motivation The Peter Clark (PC) algorithm is a popular causal discovery method to learn causal graphs in a data-driven way. Until recently, existing PC algorithm implementations in R had important limitations regarding missing values, temporal structure or mixed measurement scales (categorical/continuous), which are all common features of cohort data. The new R packages presented here, micd and tpc , fill these gaps. Implementation micd and tpc packages are R packages. General features The micd package provides add-on functionality for dealing with missing values to the existing pcalg R package, including methods for multiple imputations relying on the Missing At Random assumption. Also, micd allows for mixed measurement scales assuming conditional Gaussianity. The tpc package efficiently exploits temporal information in a way that results in a more informative output that is less prone to statistical errors. Availability The tpc and micd packages are freely available on the Comprehensive R Archive Network (CRAN). Their source code is also available on GitHub (https://github.com/bips-hb/micd ; https://github.com/bips-hb/tpc). [ABSTRACT FROM AUTHOR]

Since their creation, geoparks have been among the fastest growing natural environments. Their attractiveness is one of the most important factors for the success of this natural destination. Despite their importance, a bibliometric analysis on geopark attractiveness is missing from the studied databases. The aim of this paper is to synthesise a heterogeneous body of knowledge of geoparks in terms of attractiveness, highlighting the evolution and breadth of the research field. To achieve this, the following objectives were set: (a) to adopt a method based on functions provided by the bibliometrix package to automatically combine databases, namely WoS, Scopus, PubMed and Dimensions, detailing the method used and (b) to analyse the bibliometric indicators in order to identify the trends in the literature and the possible directions for future research. The applied methodology was based on bibliometric analysis using R for non-coders. From the 707 documents retrieved, the validation process resulted in 349 eligible documents published between 2002 and 2024, on which the analysis was carried out. The current study elaborated a method and examined the key information on the topic trends, which were given by production performance, productivity trends, spatial analysis and abstract approach analysis. Additionally, strategic mapping of the conceptual context was performed. Thus, the result provides a description of the automatic method with practical applications. As discerned from the three-dimensional analysis (spatial, temporal and size), the emerging research directions within scientific creativity encompassed (1) forms of tourism practiced in geoparks, especially focused on ecotourism and volcanic tourism; (2) geomorphological features, mineral springs and mud volcanoes; (3) aesthetic aspects, scenic sites and mining heritage; and (4) methodology, data analysis and modelling methods across different regions and countries. [ABSTRACT FROM AUTHOR]

What happens when actors and sectors incorporate references to specific territories in their development efforts? Throughout history, producers have capitalised individually and collectively on place associations through mechanisms such as product marketing and cultural framing in order to protect investments and promote internationalisation efforts. This contribution explores the agenda-setting power of the concept of 'place-based intangibles': assets with qualities of intangibles but intricately tied to specific geographical commons. After defining the concept, we overview the key substantive topics, innovative approaches and policy puzzles in three thematic areas of research: (1) regional strategies to build place-based intangibles; (2) place-based intangibles and regional development; and (3) place-based intangibles and corporate strategies. We conclude by discussing the prospects set forth by the assemblage of fresh papers included in this special issue, which this paper also introduces, and put forward the implications of place-based intangibles for policymakers, businesses and scholars. [ABSTRACT FROM AUTHOR]

In 'Alfred Nobel's Karlskoga', Sweden, the municipality has placed its most famous former resident at the heart of its economic development strategy. Through an in-depth qualitative case study, we examine the tensions and complexities surrounding this process and fill an existing research gap around personality-based place branding for regional development purposes. The findings suggest that even with a world-famous figure as talisman, personality-based place branding is a complex endeavour where old rivalries, tightknit social structures and economic dependencies makes us question – is it even possible to build a brand that is both inclusive and truly representational of a place? [ABSTRACT FROM AUTHOR]

Background: Eukaryotic cells are highly compartmentalized by a variety of organelles that carry out specific cellular processes. The position of these organelles within the cell is elaborately regulated and vital for their function. For instance, the position of lysosomes relative to the nucleus controls their degradative capacity and is altered in pathophysiological conditions. The molecular components orchestrating the precise localization of organelles remain incompletely understood. A confounding factor in these studies is the fact that organelle positioning is surprisingly non-trivial to address e.g., perturbations that affect the localization of organelles often lead to secondary phenotypes such as changes in cell or organelle size. These phenotypes could potentially mask effects or lead to the identification of false positive hits. To uncover and test potential molecular components at scale, accurate and easy-to-use analysis tools are required that allow robust measurements of organelle positioning. Results: Here, we present an analysis workflow for the faithful, robust, and quantitative analysis of organelle positioning phenotypes. Our workflow consists of an easy-to-use Fiji plugin and an R Shiny App. These tools enable users without background in image or data analysis to (1) segment single cells and nuclei and to detect organelles, (2) to measure cell size and the distance between detected organelles and the nucleus, (3) to measure intensities in the organelle channel plus one additional channel, (4) to measure radial intensity profiles of organellar markers, and (5) to plot the results in informative graphs. Using simulated data and immunofluorescent images of cells in which the function of known factors for lysosome positioning has been perturbed, we show that the workflow is robust against common problems for the accurate assessment of organelle positioning such as changes of cell shape and size, organelle size and background. Conclusions: OrgaMapper is a versatile, robust, and easy-to-use automated image analysis workflow that can be utilized in microscopy-based hypothesis testing and screens. It effectively allows for the mapping of the intracellular space and enables the discovery of novel regulators of organelle positioning. [ABSTRACT FROM AUTHOR]

We present the R package galamm, whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation of models with an arbitrary number of crossed or nested random effects, smoothing splines, mixed response types, factor structures, heteroscedastic residuals, and data missing at random. Implementation using sparse matrix methods and automatic differentiation ensures computational efficiency. We here briefly present the implemented methodology, give an overview of the package and an example demonstrating its use. [ABSTRACT FROM AUTHOR]

Background: Risk-based quality management is a regulatory-recommended approach to manage risk in a clinical trial. A key element of this strategy is to conduct risk-based monitoring to detect potential risks to critical data and processes earlier. However, there are limited publicly available tools to perform the analytics required for this purpose. Good Statistical Monitoring is a new open-source solution developed to help address this need. Methods: A team of statisticians, data scientists, clinicians, data managers, clinical operations, regulatory, and quality compliance staff collaborated to design Good Statistical Monitoring, an R package, to flexibly and efficiently implement end-to-end analyses of key risks. The package currently supports the mapping of clinical trial data from a variety of formats, evaluation of 12 key risk indicators, interactive visualization of analysis results, and creation of standardized reports. Results: The Good Statistical Monitoring package is freely available on GitHub and empowers clinical study teams to proactively monitor key risks. It employs a modular workflow to perform risk assessments that can be customized by replacing any workflow component with a study-specific alternative. Results can be exported to other clinical systems or can be viewed as an interactive report to facilitate follow-up risk mitigation. Rigorous testing and qualification are performed as part of each release to ensure package quality. Conclusions: Good Statistical Monitoring is an open-source solution designed to enable clinical study teams to implement statistical monitoring of critical risks, as part of a comprehensive risk-based quality management strategy. [ABSTRACT FROM AUTHOR]

This paper presents the employee ratings and reviews data from Glassdoor and the R codes used to collect, clean, and organize the data. We collect three types of information for each Glassdoor review: review metrics, content, and reviewer information. We also calculate some commonly used textual metrics, such as sentiment, readability, the number of uncertainty words, etc. The datasets include necessary identifiers that can connect to other financial data sources. All the variables and metrics are provided at the review level, which enables researchers to aggregate the data from different levels and angles. As a demonstrative example, we use a simple word list to measure how often employees mention COVID-19 in review comments. The R codes provided as an RStudio project are self-contained and can also be modified and applied to other data sources of interest. [ABSTRACT FROM AUTHOR]

This paper presents the ARDL package for the statistical language R, demonstrating its main functionalities in a step by step guide. Some of its main advantages over other related R packages are the intuitive API, and the fact that includes many important features missing from other packages that are essential for an in depth analysis. Additionally, it is designed in such a way that it can be combined with other packages for post regression diagnostics and tests. These characteristics are shown through an example, where we showcase part of the application demonstrated in the seminal work of Pesaran et al. (J Appl Econom 16:289–326, 2001). [ABSTRACT FROM AUTHOR]

This special issue on spatial macroeconomics aims to bridge the divide between spatial and macroeconomics. Defined in the introduction, spatial macroeconomics explores the interactions between economic activity and geographical space. The issue comprises eleven papers authored by a total of 32 researchers. These papers were selected through a combination of solicited submissions and an open call for contributions. Four papers within this special issue delve into spatial macroeconomic theory. They cover topics such as agglomeration economies for innovation, a neoclassical spatial general equilibrium growth model, the spatial sorting of heterogeneous workers and the impact of national industrial policies in strategic industries on trade. Additionally, seven papers offer empirical studies that encompass a wide range of methodologies. These include general equilibrium models, input-output-based analyses and econometric models. The empirical research addresses various topics, such as the impact of trade on productivity, the trade-off between efficiency and equity, fiscal assistance, local and nationwide fiscal multipliers, forced human displacement during wars and the spatial diffusion effects of renewable energy resource deployment. [ABSTRACT FROM AUTHOR]

In the current study, the impacts of climate change on precipitation to the Amaravati city of Andhra Pradesh, India, are assessed. Trends and variability of precipitation changes for the historical period 1951–2014 and various Shared Socioeconomic Pathways (SSP), SSP scenarios of the 2015–2100 period are determined by considering daily precipitation time series from bias-corrected climate projected data of precipitation from CMIP6 GCM, ACCESS CM2. Mann–Kendall (M–K) test and Sen's slope estimator are used to perform trend and variability analysis of precipitation attributed to climate change. No magnitude of trend is found for precipitation both for observed and under SSP scenarios. However, there are increasing and decreasing trends observed season-wise and annual precipitation datasets for both observed and under SSP scenarios within and around the study area. Also, the urban land proportion projections indicate that the total area becomes urbanized even at the end of the year 2060. The findings of this study may assist in predicting the impacts of climate change on precipitation to a city. [ABSTRACT FROM AUTHOR]

Background Excess nitrogen (N) loading to coastal ecosystems impairs estuarine water quality. Land management decisions made within estuarine watersheds have a direct impact on downstream N delivery. Natural features within watersheds can act as landscape sinks for N, such as wetlands, streams and ponds that transform dissolved N into gaseous N, effectively removing it from the aquatic system. Identifying and evaluating these landscape sinks and their spatial relationship to N sources can help managers understand the effects of alternative decisions on downstream resources. Methods The N-Sink approach uses widely available GIS data to identify landscape sinks within HUC-12 (or larger) catchments, estimate their N removal potential and map the effect of those sinks on N movement through the catchment. Static maps are produced to visualize N removal efficiency, transport and delivery, the latter in the form of an index. The R package nsink was developed to facilitate data acquisition, processing and visualization. Results nsink creates static maps for a specific HUC-12, or users can visit the University of Connecticut website to explore previously mapped areas. Users can investigate specific flowpaths interactively by clicking on any location within the catchment. A flowpath is generated with a table describing N removal along each segment. We describe the motivation behind developing nsink, discuss implementation in R, and present two use case examples. nsink is available from https://github.com/USEPA/nsink. Conclusions N-Sink is a decision support tool created for local decision-makers and NGOs to facilitate better understanding of the relationship between land use and downstream N delivery. Local decision-makers that have prioritized N mitigation in their long-term planning can use nsink to better understand the potential impact of proposed development projects, zoning variances, and land acquisition or restoration. nsink also allows resource economists to investigate the tradeoffs among different, often costly, N reduction strategies.

Based on a unified framework, this study explores the influence of regional intellectual property protection (IPP) on domestic value added in exports. A theoretical model is constructed to illustrate that IPP affects the types and prices of intermediate goods through effects of market power, product category, and crowding-out, influences the choice of domestic and imported raw materials, and finally affects the domestic added value in exports of enterprises. Empirical evidence on the influence of IPP on the domestic added value in exports is provided utilizing China Customs Statistics, the Chinese Industrial Enterprise Database, and Chinese prefectural-level data from 2000 to 2007. The results show that IPP significantly promotes the domestic value added of exports in China, but the effect is heterogeneous in terms of region and mode of enterprise trade; in the eastern region and processing trade enterprises the IPP effect is more pronounced. [ABSTRACT FROM AUTHOR]

Recent advancements in education scholarship have introduced Item Response Theory (IRT) models to address treatment heterogeneity at the assessment item level. These models for item-level heterogeneous treatment effects (IL-HTE) enable detailed analyses of treatments that may have varying impacts on individual items within an assessment. This article offers a comprehensive tutorial for applied researchers interested in implementing IL-HTE analysis in R, utilizing the lme4 package. Using empirical data from a second-grade reading comprehension assessment as a running example, this tutorial emphasizes model-building strategies, interpretation techniques, visualization methods, and extensions. By following this tutorial, researchers will gain practical insights into utilizing IL-HTE analysis for enhanced understanding and interpretation of treatment effects at the item level. [ABSTRACT FROM AUTHOR]

In environmental health, the specific molecular mechanisms connecting a chemical exposure to an adverse endpoint are often unknown, reflecting knowledge gaps. At the public Comparative Toxicogenomics Database (CTD; https://ctdbase.org/), we integrate manually curated, literature-based interactions from CTD to compute four-unit blocks of information organized as a potential step-wise molecular mechanism, known as "CGPD-tetramers," wherein a chemical interacts with a gene product to trigger a phenotype which can be linked to a disease. These computationally derived datasets can be used to fill the gaps and offer testable mechanistic information. Users can generate CGPD-tetramers for any combination of chemical, gene, phenotype, and/or disease of interest at CTD; however, such queries typically result in the generation of thousands of CGPD-tetramers. Here, we describe a novel approach to transform these large datasets into user-friendly chord diagrams using R. This visualization process is straightforward, simple to implement, and accessible to inexperienced users that have never used R before. Combining CGPD-tetramers into a single chord diagram helps identify potential key chemicals, genes, phenotypes, and diseases. This visualization allows users to more readily analyze computational datasets that can fill the exposure knowledge gaps in the environmental health continuum. [ABSTRACT FROM AUTHOR]

The recent so-called 'data revolution' offers unprecedented opportunities to innovate regional policies. New data sources are being widely used by the scientific community, however their uptake is far from being systematic in the policy cycle, where data innovation can improve territorial impact assessment. This paper presents a survey on the use of non-traditional data in the context of regional policy, together with a case study on real estate markets of three European countries, highlighting the perspectives and limitations of computational social science in regional analysis in terms of data quality and availability. [ABSTRACT FROM AUTHOR]

Most researchers receive formal training in frequentist statistics during their undergraduate studies. In particular, hypothesis testing is usually rooted on the null hypothesis significance testing paradigm and its p‐value. Null hypothesis Bayesian testing and its so‐called Bayes factor are now becoming increasingly popular. Although the Bayes factor is often introduced as being the Bayesian counterpart to the p‐value, its computation, use, and interpretation are quite distinct from the p‐value. There is now evidence confirming that the application of the Bayes factor in applied research is ill‐devised. To improve the current status quo, we have created a Shiny/R app called the Bayes factor, which offers a dynamic tutorial for learning all the basics about the Bayes factor. In this paper, we explain how the app works and we offer suggestions on how to use it in class or self‐study settings. The app is freely available at https://statsedge.org/shiny/LearnBF/. [ABSTRACT FROM AUTHOR]

With global climate change leading to increasing intensity and frequency of droughts, as well as the growing problem of soil salinization, these factors significantly affect crop growth, yield, and resilience to adversity. Oats are a cereal widely grown in temperate regions and are rich in nutritive value; however, the scientific literature on the response of oat to drought and salt stress has not yet been analyzed in detail. This study comprehensively analyzed the response of oat to drought stress and salt stress using data from the Web of Science core database and bibliometric methods with R (version4.3.1), VOSviewer (version 1.6.19), and Citespace (version6.3.1.0) software. The number of publications shows an increasing trend in drought stress and salt stress in oat over the past 30 years. In the field of drought-stress research, China, the United States, and Canada lead in terms of literature publication, with the most academic achievements being from China Agricultural University and Canadian Agricultural Food University. The journal with the highest number of published papers is Field Crops Research. Oat research primarily focuses on growth, yield, physiological and biochemical responses, and strategies for improving drought resistance. Screening of drought-tolerant genotypes and transformation of drought-tolerant genes may be key directions for future oat drought research. In the field of salt-stress research, contributions from China, the United States, and India stand out, with the Chinese Academy of Agricultural Sciences and Inner Mongolia Agricultural University producing the most significant research results. The largest number of published articles has been found in the Physiologia Plantarum journal. Current oat salt-stress research primarily covers growth, physiological and biochemical responses, and salt-tolerance mechanisms. It is expected that future oat salt research will focus more on physiological and biochemical responses, as well as gene-editing techniques. Despite achievements under single-stress conditions, combined drought and salt-stress effects on oat remain understudied, necessitating future research on their interaction at various biological levels. The purpose of this study is to provide potential theoretical directions for oat research on drought and salt stress. [ABSTRACT FROM AUTHOR]

Since January 2022, the Regulation on European Business Statistics (EU 2019/2152) requires EU Member States to compulsorily share microdata on intra-EU exports. Establishing intra-EU export Micro-Data Exchange (MDE) provides National Statistical Institutes with a new data source to compile intra-EU import statistics. The availability of MDE tackles two key challenges: diminishing the overall response burden on data providers and meeting user expectations regarding the quality of the produced statistics. However, transitioning to a data production system based on MDE data requires the assessment of the coherence and comparability between MDE and National import data. To identify asymmetries between the two data sources, Istat developed an innovative application designed to foster cooperation among Member States. The tool was developed using the Shiny package in R. The implemented solution allows users to perform exploratory analysis, systematic error detection, and selective editing. The most relevant asymmetries are identified through relative contribution and the asymmetry suspicion indices assessed by user-defined thresholds. Sharing the open tool within the European Statistical System enhances interoperability, promotes method harmonization, and encourages the adoption of official statistical standards. [ABSTRACT FROM AUTHOR]

The First Egongyan Bridge in Chongqing, China was opened to traffic on December 27, 2000. It carries six-lanes of city traffic and two pedestrian paths, one on each side. It is a 600 m span suspension bridge. It was planned that the two pedestrian paths might eventually be converted to transit tracks in the future. However, with the rapid increase in urban traffic, the deck was transformed into an 8-lane bridge instead. Therefore, a new bridge parallel and adjacent to the old bridge was built for the rail transit. For aesthetic reasons, the city decided that the new bridge should also be a suspension bridge, with the same span length and tower height as the first bridge. The First Egongyan Bridge was a true suspension bridge with main cables anchored to ground anchors. However, the new bridge bridge's proximity to the first bridge necessitated the construction of a self-anchored suspension bridge that did away with a second set of ground anchors. This 600 m span happened to be the world's longest span for a self-anchored suspension bridge. [ABSTRACT FROM AUTHOR]

Aim: Species occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses. Innovation: We introduce an R package, occTest, that synthesizes a growing open‐source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user‐defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed. Main conclusions: occTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom‐built rules. As a result, occTest can better assess each record's appropriateness for its intended application. [ABSTRACT FROM AUTHOR]

In this note, we describe how to install and use the 'toponym' R package, which is designed for mapping and manipulating toponymic data from the GeoNames database. This introduction will allow even unexperienced users of R to efficiently produce maps and perform simple analyses. [ABSTRACT FROM AUTHOR]

Using R to conduct univariate meta-analyses is becoming common for publication. However, R can also conduct multivariate meta-analysis (MMA). However, newcomers to both R and MMA may find using R to conduct MMA daunting. Given that, R may not be easy for those unfamiliar with coding. Likewise, MMA is a topic of advanced statistics. Thus, it may be very challenging for most newcomers to conduct MMA using R. If this holds, this can be viewed as a practice gap. In other words, the practice gap is that researchers are not capable of using R to conduct MMA in practice. This is problematic. This paper alleviates this practice gap by illustrating how to use R (the metaSEM package) to conduct MMA on educational psychology data. Here, the metaSEM package is used to obtain the required MMA text outputs. However, the metaSEM package is not capable of producing the other required graphical outputs. As a result, the meta for package is also used as a complimentary to generate the required graphical outputs. Ultimately, we hope that our audience will be able to apply what they learn from this method paper to conduct MMA using R in their teaching, research, and publication. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are usually based on a single data type, such as presence‐only (PO), presence‐absence (PA) or abundance (AA). Results from SDMs using single sources of data will suffer from inherent biases and limitations to that data type. For example, PO data contain sampling‐bias and PA/AA data are often less expansive and more sparse. Integrated SDMs (ISDMs) combine multiple data types and have recently emerged as a way to leverage strengths and minimise weaknesses of the different data types. They pose a common (distribution) model and separate observation models for each of the data types. The 'RISDM' package for the R environment (www.r‐project.org) provides access to this modelling framework using functions for preparation, fitting, interpreting and diagnosing models. The functionality of the package is demonstrated here using synthetic data sets. [ABSTRACT FROM AUTHOR]

Copyright of Düzce University Journal of Forestry / Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi is the property of Duzce University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on a prospective and retrospective analysis of the 'Raising the bar' editorials preceding regular issues of this journal from 2016–2023, this paper identifies two laws of spatial economic modelling: (i) Units of observation cannot be treated as independent entities because they interact and (ii) interaction causes spillovers from one unit to another. Common themes and trends uncovered in these editorials, the progress that has been made and the direction in which the research fields can continue to develop are discussed, using both laws as references. Currently, there is no single approach that adequately addresses both laws. [ABSTRACT FROM AUTHOR]