Programming Experience Research Articles

Mechanic Maker: Accessible Game Development via Symbolic Learning Program Synthesis

Game development is a highly technical practice that traditionally requires programming skills. This serves as a barrier to entry for would-be developers or those hoping to use games as part of their creative expression. While there have been prior game development tools focused on accessibility, they generally still require programming, or have major limitations in terms of the kinds of games they can make. In this paper we introduce Mechanic Maker, a tool for creating a wide-range of game mechanics without programming. It instead relies on a backend symbolic learning system to synthesize game mechanics from examples. We conducted a user study to evaluate the benefits of the tool for participants with a variety of programming and game development experience. Our results demonstrated that participants' ability to use the tool was unrelated to programming ability. We conclude that tools like ours could help democratize game development, making the practice accessible regardless of programming skills.

Closha 2.0: a bio-workflow design system for massive genome data analysis on high performance cluster infrastructure.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and significant computational challenges. As the cost of next-generation sequencing (NGS) has decreased, the amount of genomic data has surged globally. However, the cost and complexity of the computational resources required continue to be substantial barriers to leveraging big data. A promising solution to these computational challenges is cloud computing, which provides researchers with the necessary CPUs, memory, storage, and software tools. Here, we present Closha 2.0, a cloud computing service that offers a user-friendly platform for analyzing massive genomic datasets. Closha 2.0 is designed to provide a cloud-based environment that enables all genomic researchers, including those with limited or no programming experience, to easily analyze their genomic data. The new 2.0 version of Closha has more user-friendly features than the previous 1.0 version. Firstly, the workbench features a script editor that supports Python, R, and shell script programming, enabling users to write scripts and integrate them into their pipelines. This functionality is particularly useful for downstream analysis. Second, Closha 2.0 runs on containers, which execute each tool in an independent environment. This provides a stable environment and prevents dependency issues and version conflicts among tools. Additionally, users can execute each step of a pipeline individually, allowing them to test applications at each stage and adjust parameters to achieve the desired results. We also updated a high-speed data transmission tool called GBox that facilitates the rapid transfer of large datasets. The analysis pipelines on Closha 2.0 are reproducible, with all analysis parameters and inputs being permanently recorded. Closha 2.0 simplifies multi-step analysis with drag-and-drop functionality and provides a user-friendly interface for genomic scientists to obtain accurate results from NGS data. Closha 2.0 is freely available at https://www.kobic.re.kr/closha2 .

Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation Rules

Trigger-action platforms (TAPs) enable users without programming experience to personalize the behavior of Internet of Things applications and services through IF-THEN rules. Unfortunately, the arbitrary connection of smart devices and online services, even with simple rules, such as “IF the entrance Netatmo Wheather Station detects a temperature above 30 \({}^{\circ}C\) ( \(86^{\circ}F\) ) THEN open the shutters in the living room,” might expose users to potential security and privacy risks (e.g., the execution of the previous rule might provide an easy entry point for thieves, especially during the summer vacation period). The goal of our research is to make the users capable of understanding and mitigating the threats and risks associated with the execution of IF-THEN rules. To this end, we define a new challenging task, namely generating post hoc justifications of privacy and security risks associated with automation rules, and propose a novel natural language generation strategy based on hybrid prompt learning producing justifications in the form of real-life threat scenarios. The proposed strategy allows for prompt customization with task-specific information, providing contextual details enabling to grasp the nuances and subtleties of the domain language, resulting in more coherent justifications. The experiments conducted on the if-this-then-that (IFTTT) platform show that our method produces effective justifications, improving the explainability of discrete and hybrid prompting methods up to 27% in BLEURT score. The code of the software is publicly available on GitHub 1 .

Utilising Blended Learning for Large Classes to Deliver an Introductory Programming Course

Higher education institutions are constantly pushed to increase the student intake and produce industry-ready graduates. The pressure generally arises from the governing bodies, originating from socio-political factors. It is particularly evident in the field of Information Communication Technology (ICT) where there is a growing need for quality graduates. Undergraduate programmes in the field of ICT generally offer introductory computer programming courses that are mandatory at early stages of the degree programme. Consequently, the number of students registering for such courses can increase drastically. The students are coming from diversified backgrounds. While some students may have prior experience in computer programming, a larger majority may not have any prior knowledge or experience. Increased class size meets the constraint of the limited availability of physical classroom space and would require the class to be separated into smaller groups for face-to-face activities. Large classes are also challenged with the increased man-power requirement in terms of lecturers, instructors, and teaching assistants. Maintaining student engagement and interactivity becomes increasingly difficult in a large class. Grading of assessments becomes time-consuming and needs and requires increased manpower. Based on the observations and learning from an ongoing introductory programming course, this study evaluates the procedure for overcoming the challenges faced by the teaching staff, especially with a class size exceeding 1000. To cover the theoretical components, recorded video lectures are provided. The class is divided into groups consisting of approximately 100 students and live, online, interactive group discussions are conducted. The live, online smaller group discussions allow students to clarify doubts regarding the content. This also allows the distribution of the limited teaching and assistant staff. Students who need further assistance are encouraged to meet the teaching assistants physically. Thus, the workload is distributed effectively, and it helps ensure the dedicated support and attention is provided to the students. Auto graded programming assignments are effectively employed for formative and summative assessments, which reduces the grading workload significantly. The findings of this study provide insights on course design to effectively deliver the content and conduct assessments overcoming the challenges presented by increased class size.

AUTOMAP: Inferring Rank-Polymorphic Function Applications with Integer Linear Programming

Dynamically typed array languages such as Python, APL, and Matlab lift scalar operations to arrays and replicate scalars to fit applications. We present a mechanism for automatically inferring map and replicate operations in a statically-typed language in a way that resembles the programming experience of a dynamically-typed language while preserving the static typing guarantees. Our type system---which supports parametric polymorphism, higher-order functions, and top-level let-generalization---makes use of integer linear programming in order to find the minimum number of operations needed to elaborate to a well-typed program. We argue that the inference system provides useful and unsurprising guarantees to the programmer. We demonstrate important theoretical properties of the mechanism and report on the implementation of the mechanism in the statically-typed array programming language Futhark.

Computational Thinking Integrated in School Subjects – A Cross-Case Analysis of Students’ Experiences

The integration of computational thinking (CT) into K-12 education offers substantial potential to improve digital literacy and deepen students’ understanding across various subject areas. By embedding computational procedures and solution-oriented approaches into traditional curricula, students can develop essential skills that are critical for thriving in an increasingly digital world. However, the effective integration of CT into subject-specific learning poses challenges for educators, largely due to a lack of training and experience in implementing interdisciplinary instructional strategies. This study explores the impact of integrating CT into K-12 education through diverse instructional approaches, including modelling, storytelling, and unplugged activities. The research examines how students' subject knowledge, CT concepts, and practices evolved under each CT integration practice, as well as their acceptance toward CT integrated lessons. The study was conducted in different subjects, including language arts, biology, and physics, and at different grade levels. The technology acceptance model was used as a theoretical framework to understand students' adoption towards the different integration practices. The study revealed that different CT integration practices yield varying impacts on students' attitudes, particularly in relation to their prior programming experiences. Namely, primary school students with prior programming experience display a higher behavioural intention due to a lack of early biases toward programming. Conversely, secondary school students, less familiar with interdisciplinary approaches, initially show less enthusiasm for future engagement. These insights underscore the need for tailored instructional strategies that consider students' prior experiences and the broader goal of preparing them to contribute to a technologically driven society.

A high-throughput gene expression analysis software tool for developmental time series and gene signature analysis of human cardiomyocyte differentiation

Publicly available high-throughput gene expression data enable the investigation of biological processes by the scientific community. Although several bioinformatics tools offer methodologies for basic differential gene expression analysis, difficulties arise in the analysis of multiple sample groups comprising a developmental time series, especially when the identification and classification of unique gene expression patterns is the primary goal of the study. Data analysis using these tools requires programming experience, which limits the accessibility of these tools to the broader community. To streamline developmental time-series investigations, we created the Developmental Gene Expression Analysis (devGEA) tool. This environment can be implemented locally or via web browsers to expedite differential gene expression analysis. This tool provides gene signature determination methods that can classify differentially expressed genes based on their correlation with gene expression patterns. devGEA was used to characterize cardiac development gene expression signatures from high-throughput RNA-seq datasets profiling small-molecule directed cardiomyocyte differentiation of human pluripotent stem cell lines (hiPSCs). After pre-processing, discrete gene expression criteria-based expected changes were used to classify the genes into developmental signatures. Several cardiomyocyte differentiation markers and candidate cardiac genes representing different developmental signatures were experimentally validated using the GIBCOTM hiPSC line. This method was then compared to a gene signature correlation approach that classified expressed genes based on their degree of similarity with key cardiac developmental signatures representing the stages of cardiomyocyte differentiation. Therefore, devGEA provides a robust workflow for investigator-driven analysis of developmental time series, allowing for the identification of differentially expressed genes and gene signatures for extensive experimental investigation. We also introduced a method for classifying genes by their correlation with genes or developmental patterns of interest. Our correlation-based method takes advantage of a priori knowledge of an experiment and is conceptually simpler than unsupervised clustering approaches.

HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity.

Epidemiologic and genetic studies in many complex diseases suggest subgroup disparities (e.g. by sex, race) in disease course and patient outcomes. We consider this from the standpoint of integrative analysis where we combine information from different views (e.g. genomics, proteomics, clinical data). Existing integrative analysis methods ignore the heterogeneity in subgroups, and stacking the views and accounting for subgroup heterogeneity does not model the association among the views. We propose Heterogeneity in Integration and Prediction (HIP), a statistical approach for joint association and prediction that leverages the strengths in each view to identify molecular signatures that are shared by and specific to a subgroup. We apply HIP to proteomics and gene expression data pertaining to chronic obstructive pulmonary disease (COPD) to identify proteins and genes shared by, and unique to, males and females, contributing to the variation in COPD, measured by airway wall thickness. Our COPD findings have identified proteins, genes, and pathways that are common across and specific to males and females, some implicated in COPD, while others could lead to new insights into sex differences in COPD mechanisms. HIP accounts for subgroup heterogeneity in multi-view data, ranks variables based on importance, is applicable to univariate or multivariate continuous outcomes, and incorporates covariate adjustment. With the efficient algorithms implemented using PyTorch, this method has many potential scientific applications and could enhance multiomics research in health disparities. HIP is available at https://github.com/lasandrall/HIP, a video tutorial at https://youtu.be/O6E2OLmeMDo and a Shiny Application at https://multi-viewlearn.shinyapps.io/HIP_ShinyApp/ for users with limited programming experience.

The pathway to delivering injectable CAB for HIV prevention: strategies from global PrEP leaders leveraging an adapted version of the Intervention Scalability Assessment Tool (ISAT)

BackgroundLonger-acting cabotegravir (CAB) is a novel, safe, and efficacious pre-exposure prophylaxis (PrEP) for HIV prevention. As we near a time for CAB scale-up, the experience of global leaders in PrEP research and implementation can be leveraged to identify optimal strategies for scaling and integrating CAB into existing PrEP infrastructure worldwide.MethodsWe recruited leaders of HIV prevention clinical trials and large PrEP programs through a combination of purposive and snowball sampling for participation in individual interviews. We conducted interviews using a semi-structured guide that compared CAB to oral PrEP and sought perspectives on barriers and strategies for CAB scale-up. Interviews were conducted virtually, audio recorded, and transcribed. We used thematic analysis, grounded in an adapted version of the Intervention Scalability Assessment Tool (ISAT), to identify critical elements for optimizing delivery of CAB.ResultsFrom October 2021 to April 2022, we interviewed 30 participants with extensive experience in PrEP research, care, and programming. Participants worked in all seven WHO regions and reported a median of 20 years working in HIV and 10 years in PrEP. Participants agreed that CAB was efficacious and discrete, therefore having the potential to address current concerns about oral PrEP adherence and stigma. Participants indicated direct and indirect costs for provider training, expansion of existing medical infrastructure, and the current medication cost of CAB as major concerns for roll out. The true cost to the end-user and health system were unknown. There were some conflicting strategies on how to best address product targeting, presentation of efficacy, and timing of product availability with scale-up. Some thought that targeting CAB for the general population could normalize PrEP and decrease stigma, while others thought that prioritizing key populations could optimize impact by targeting those with highest risk. Overall, participants emphasized that to ensure successful CAB scale-up, communities and stakeholders must be involved at every stage of planning and implementation.ConclusionsOur evaluation found that although there is a clear and urgent need for additional HIV PrEP options beyond daily oral PrEP, CAB scale-up must be thoughtful, flexible, and based in lessons learned from oral PrEP rollout.

What Women Want: Program Design for Females Sentenced for Child Sexual Abuse

ABSTRACT There is a growing body of research exploring the treatment needs of females sentenced for child sexual abuse (CSA), along with an increased recognition of the importance of considering the “female” experience in correctional programming for this population. While knowledge of the gendered nuances relevant to “what” to address in program content has been explored, strategies for “how” to enhance responsivity in treatment programming for this population are less well-known. This research aimed to uncover key responsivity considerations relevant to program design and configuration for women sentenced for CSA. Interviews were conducted with justice-involved women sentenced for CSA in Australia (n = 18) and practitioners (n = 25) involved in delivering treatment. Thematic analysis revealed that flexible treatment methods, re-imagined treatment settings, and positive alliances are perceived as essential features of programs for women sentenced for CSA. Participant suggestions largely reflect practices currently used within programs for males sentenced for these offenses, and mirror international best-practice standards in correctional treatment. These findings shine light on the preferences of women who are commonly silenced in correctional research; thereby helping to inform evidence-based program development and design to enhance responsivity.

The practical use of artificial intelligence in Transfusion Medicine and Apheresis

BackgroundBlood and plasma volume calculations are a daily part of practice for many Transfusion Medicine and Apheresis practitioners. Though many formulas exist, each facility may have their own modifications to consider. ChatGPT (Generative Pre-trained Transformer) provides a new and exciting pathway for those with no programming experience to create personalized programs to meet the demands of daily practice. Additionally, this pathway creates computer programs that provide accurate and reproducible outputs. Herein, we aimed to create a step-by-step process for clinicians to create customized computer programs for use in everyday practice. MethodsWe created a process of inputs to ChatGPT-40, which generated computer programming code. This code was copied and pasted into Notepad (and saved as a Python file) and Google Colaboratory to verify functionality. We validated the durability of our process by repeating it over a 5-day timeframe and by recruiting volunteers to reproduce our outputs using the suggested process. ResultsComputer code generated by ChatGPT-40 in response to our common language inputs was accurate and durable over time. The code was fully functional in both Python and Colaboratory. Volunteers reproduced our process and outputs with minimal assistance. ConclusionWe analyzed the practical application of ChatGPT-40 and artificial intelligence (AI) to perform daily calculations encountered in Transfusion Medicine. Our results provide a proof of concept that people with no programming experience can create customizable solutions for their own facilities. Our future work will expand to the creation of comprehensive and customizable websites designed for each individual user.

Predictors of university students’ intentions to enroll in computer programming courses: a mixed-method investigation

Students in technology majors such as Computer Science and Information Technology need to take a series of computer programming courses to graduate. Yet, not all students will persist in taking programming courses as required, and little is known about the factors influencing their enrollment intentions. Research is needed to better understand which factors predict students’ enrollment intentions for programming courses. Based on Expectancy-Value Theory, we surveyed three hundred university students in China to measure their intentions of enrolling in future programming courses and factors influencing their enrollment intentions. Mediated Multiple Indicators Multiple Causes models were run to identify the relationships between these factors and enrollment intentions. Expectancies, intrinsic value, attainment value, and utility value of the courses directly predict enrollment intentions. Peer comparison, quality of teaching, and programming experiences were found to influence enrollment intentions through expectancies and values indirectly. No gender difference was found in expectancies and enrollment intentions. Qualitative data analysis confirmed the findings from the quantitative data analysis and further revealed that course difficulty is another major factor influencing enrollment intentions. These findings have implications for supporting students' expectancies and values in computer programming and curriculum design.

Prescribed Fire Programming Experiences, Needs, and Challenges of Southeastern United States Extension Professionals

Prescribed fires are used to manage natural areas and mitigate potentially intense and catastrophic wildfires. Education and other outreach activities can increase the acceptance and adoption of prescribed fire use among private landowners. Land grant university-based Extension services are often a trusted source of information in communities on a variety of topics, but no published research has previously assessed Extension’s capacity for prescribed fire education and training. This study uses semi-structured interviews to explore the knowledge and experiences of 23 southeastern U.S. Extension professionals with prescribed fire outreach. Thematic analysis of the interviews guided by Ostrom’s social-ecological systems framework revealed that these Extension professionals face a variety of challenges stemming from their institutions, audiences, and own backgrounds that complicate prescribed fire outreach. These challenges can be overcome, allowing Extension professionals to conduct fire science outreach and play an active role in facilitating networks to build capacity through cooperation.

FluoAnalysis: An Open-Source MATLAB Toolbox for Analysis of Calcium Imaging Measurements of Oscillatory Astrocytic and Neuronal Networks.

Calcium imaging, especially two-photon imaging, has become essential in neuroscience for studying neuronal and astrocytic activity under in vivo and in vitro conditions. Current advances in the development of calcium sensors as well as imaging hardware enable high-frequency measurements of calcium signals in hundreds of cells simultaneously. The analysis of these large datasets requires special tools and usually a certain level of programming experience. Despite advancements in calcium imaging analysis software development, significant gaps remain, particularly for data acquired at a high sampling rate that would allow for the spectral analysis of calcium signals. The FluoAnalysis MATLAB toolbox addresses these gaps by offering a comprehensive solution for analyzing simultaneously measured calcium imaging and electrophysiological data. It features both GUI-based and command-line approaches, emphasizing frequency domain analysis to reveal network-level oscillatory signals linked to single-cell activity. In addition, the toolbox puts special emphasis on differentiating between astrocytes and neurons, revealing the interactions between the network activity of the two major cell types of the brain. It facilitates a streamlined workflow for data loading, ROI identification, cell classification, fluorescence intensity calculation, spectral analysis, and report generation, supporting both manual and automated high-throughput analysis. This versatile platform enables the comprehensive analysis of large imaging datasets. In conclusion, the FluoAnalysis MATLAB toolbox provides a robust and versatile platform for the integrated analysis of calcium imaging and electrophysiological data, supporting diverse neuroscience research applications.

Developing and validating an artificial intelligent empowerment instrument: evaluating the impact of an artificial intelligent literacy programme for secondary school and university students

Artificial intelligence (AI) is rapidly transforming various sectors of society, requiring a new form of literacy: AI literacy. This study validated a new instrument designed to measure students’ AI empowerment conceptualised as consisting of four components: impact, self-efficacy in AI, creative self-efficacy in AI, and meaningfulness. Confirmatory factor analysis was used to validate the proposed components of the AI empowerment instrument. The sample comprised 224 secondary school and university students who completed an 18-hour AI literacy programme. The results showed that the students’ AI empowerment was significantly increased by the AI literacy programme. Specifically, the AI literacy programme was found to narrow the gender gap in AI empowerment. Furthermore, the results highlighted that prior programming experience did not significantly affect AI empowerment, indicating that AI literacy can be achieved regardless of programming experience. This study provides a theoretical framework for understanding and quantifying the extent to which individuals feel empowered after engaging with AI activities for its conceptual understanding. It provides educators with a tool to measure students’ understanding and confidence in their AI abilities. The study also suggests directions for future research.

Quantitative Analysis of Online Courses for Learning the JavaScript Programming Language

Today, the JavaScript programming language ranks first both in the world well-known programming rankings and those in Ukraine in particular. The purpose of the research is quantitative analysis of online courses on learning JavaScript on well-known MOOC platforms. In order to achieve the purpose of our research, we used theoretical and quantitative (descriptive) methods. Also, we analyzed such well-known online platforms as Alison, Codecademy, Udemy, edX, Coursera as of December 2023. The number of online courses on each platform, their cost, an approximate user level, video length, etc. was used as criteria for the analysis. The analyzed MOOC was found to offer 1,795 online courses on learning the JavaScript programming language. Among them, 1,077 courses (60%) are paid, 201 courses (11%) can be taken for free, and 517 courses (29%) require optional payment. Most of the online JavaScript courses (788 courses, 44%) are developed for the Beginner level. Only 37 courses are available for experienced JavaScript programmers, which is 2% of the total number of online JavaScript courses. It was found that Codecademy and Udemy online platforms offer their users additional opportunities to learn JavaScript: programming projects (67 online courses); coding exercises (99 online courses); Practice test (55 online courses). The obtained results made it possible to formulate some proposals for choosing the optimal MOOC which helps to learn JavaScript, considering several selection criteria.

A Computer Laboratory for the Calculation of the Heat Capacity of a Peptide Unfolding Transition

ABSTRACT We present the implementation of a computer simulation laboratory for the calculation of the heat capacity of the folding-unfolding pseudo phase transition of a polyalanine Ala30 α-helix, using molecular dynamics simulations. Unlike conventional calorimetry experiments, which provide a macroscopic interpretation of the heat capacity, the computer laboratory additionally offers a molecular perspective of the energy exchange processes that lead to the peak of the heat capacity at the phase transition, as well as the statistical mechanical interpretation of the phase transition. The exercise is targeted toward 1st- or 2nd-year students of biophysics in the context of an undergraduate physical chemistry course. We use a graphical web interface to overcome the steep learning barriers associated with command line interactions in commonly used molecular dynamics protocols. The web interface (VIKING) also handles communication with computing hardware in the background, so that students with limited or no programming experience can launch and analyze the simulations easily. A collaboration between the VIKING support team and responsible teachers is required to set up VIKING. We also present a solution in which simulations can be launched on students’ own computers, if some command line knowledge can be expected. The laboratory can also be repurposed for students of various backgrounds (physics, chemistry, biology, or engineering) and scholastic levels (undergraduate or graduate).

PaRaVis: An automatic Python graphical package for ensemble analysis of plant beta diversity using remote sensing proxies

The ongoing impacts of climatic changes, coupled with intensified human activities, are leading to a significant loss of plant diversity, prompting urgent calls for comprehensive monitoring of forest ecosystems. This is particularly concerning in regions with the encroachment of human activities into forest zones, and inadequate regulations that primarily view forests as a source of timber without fully accounting for their critical roles in biodiversity. These issues highlight the need for effective geospatial approaches to support conservation strategies and sustained monitoring of plant diversity. The majority of remote sensing studies of plant diversity have focused on alpha-diversity, while beta-diversity plays an important role in providing a comprehensive picture of biodiversity patterns across scales. Among various remote sensing proxies, Rao's Q index stands out as a reliable metric to quantify beta-diversity using multispectral remote sensing indices. However, a holistic approach that can incorporate multiple remote sensing indices and enable comparative analysis using graphical means with computational efficiency is lacking. In response, we developed PaRaVis, an open-source Python-based graphical package for deriving spectral diversity from multispectral remote sensing datasets as a proxy for functional diversity using Rao's Q index. This tool encompasses all the necessary steps for parallelized computation, visualization, and analysis of Rao's index from either single or multiple multispectral indices. It is capable of calculating and visualizing 75 vegetation indices (VIs), from a raster scene, followed by calculating Rao's Q both unidimensionally and multidimensionally in parallel. PaRaVis also offers features for visualizing, analyzing, and comparing Rao's Q outputs using statistical performance diagnostics. It provides a unique means to infer diversity patterns in space and time and is therefore invaluable for researchers or organizations involved with plant diversity monitoring, especially those with limited data analysis or computer programming experience. To demonstrate the tool's effectiveness, we analyzed plant diversity patterns using satellite-derived spectral heterogeneity measures in forest sites within the Hyrcanian Forests of Iran and sites located in Germany. The first case study focused on UNESCO Natural Heritage sites. Our analysis revealed that employing EVI, SR3, and TCI indices as inputs for the multidimensional Rao's Q yielded higher performance in monitoring more heterogeneous forests compared to the unidimensional mode. In the second case study, we utilized field Species Richness and Shannon-Wiener diversity indices to evaluate PaRaVis and our method. We found that using a multi-temporal, multi-index approach enhances the results compared to multi-seasonal and classical Rao based on single time.

Conducting Multivariate One- and Two-Sample Statistical tests using SPSS Syntax

The availability of multivariate one-sample and two-sample testing procedures is often restricted or merged with other statistical tests in many statistical applications. SPSS software, known for its user-friendly Graphical User Interface (GUI) for statistical analysis, also provides a specialised programming language called SPSS syntax. This syntax allows users to execute statistical procedures by writing commands, providing an alternative to the GUI interface. The purpose of this paper is to create new dialogs written in the SPSS syntax language using custom dialog builder for extensions. These dialogs are designed to test multivariate one- and two-sample data when ∑ is unknown by applying Hotelling’s T2 test statistic, especially in situations where the GUI lacks these particular functionalities. In the case of two-sample data, both independent and paired multivariate testing methods are employed. Examples are supplied to demonstrate the key analyses performed using Hotelling's T2 tests. The syntax written for the analyses was performed using SPSS software v.27. The adoption of the developed dialogs is recommended for researchers requiring analysis of multivariate one- and two-sample data with limited or no programming experience, with potential future incorporation of the dialogs into forthcoming SPSS versions by IBM. Extending the scope of statistical analyses supported by SPSS dialogs through future research that encourages their use and supports wider acceptance of additional statistical methods not included in standard versions of SPSS.

An Update on the iglu Software Package for Interpreting Continuous Glucose Monitoring Data.

Background: Continuous glucose monitors (CGMs) are increasingly used to provide detailed quantification of glycemic control and glucose variability. An open-source R package iglu has been developed to assist with automatic CGM metrics computation and data visualization, providing a comprehensive list of implemented CGM metrics. Motivated by the recent international consensus statement on CGM metrics and recommendations from recent reviews of available CGM software, we present an updated version of iglu with improved accessibility and expanded functionality. Methods: The functionality was expanded to include automated computation of hypo- and hyperglycemia episodes with corresponding visualizations, composite metrics of glycemic control (glycemia risk index and personal glycemic state), and glycemic metrics associated with postprandial excursions. The algorithm for mean amplitude of glycemic excursions has been updated for improved accuracy, and the corresponding visualization has been added. Automated hierarchical clustering capabilities have been added to facilitate statistical analysis. Accessibility was improved by providing support for the automatic processing of common data formats, expanding the graphical user interface, and providing mirrored functionality in Python. Results: The updated version of iglu has been released to the Comprehensive R Archive Network (CRAN) as version 4. The corresponding Python wrapper has been released to the Python Package Index (PyPI) as version 1. The new functionality has been demonstrated using CGM data from 19 subjects with prediabetes and type 2 diabetes. Conclusions: An updated version of iglu provides comprehensive and accessible software for analyses of CGM data that meets the needs of researchers with varying levels of programming experience. It is freely available on CRAN and on GitHub at https://github.com/irinagain/iglu.