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Artigos de revistas sobre o assunto "Application Shiny"
Shirokovskikh, Margarita S. "THE APPLICATION OF SHINY MATERIALS IN TAPESTRY: FROM DECOR TO CONCEPT". Architecton: Proceedings of Higher Education, n.º 2(70) (29 de junho de 2020): 21. http://dx.doi.org/10.47055/1990-4126-2020-2(70)-21.
Texto completo da fonteMorota, Gota. "79 Statistical Graphics and Interactive Visualization in Animal Science". Journal of Animal Science 99, Supplement_3 (8 de outubro de 2021): 45. http://dx.doi.org/10.1093/jas/skab235.079.
Texto completo da fonteMorota, Gota. "312 Statistical graphics and interactive visualization in animal science". Journal of Animal Science 98, Supplement_4 (3 de novembro de 2020): 45–46. http://dx.doi.org/10.1093/jas/skaa278.083.
Texto completo da fonteJagla, Bernd, Valentina Libri, Claudia Chica, Vincent Rouilly, Sebastien Mella, Michel Puceat e Milena Hasan. "SCHNAPPs - Single Cell sHiNy APPlication(s)". Journal of Immunological Methods 499 (dezembro de 2021): 113176. http://dx.doi.org/10.1016/j.jim.2021.113176.
Texto completo da fonteKemperman, Lauren, e Matthew N. McCall. "miRcomp-Shiny: Interactive assessment of qPCR-based microRNA quantification and quality control algorithms". F1000Research 6 (23 de novembro de 2017): 2046. http://dx.doi.org/10.12688/f1000research.13098.1.
Texto completo da fonteLi, Jianfeng, Bowen Cui, Yuting Dai, Ling Bai e Jinyan Huang. "BioInstaller: a comprehensive R package to construct interactive and reproducible biological data analysis applications based on the R platform". PeerJ 6 (31 de outubro de 2018): e5853. http://dx.doi.org/10.7717/peerj.5853.
Texto completo da fonteOberti, Mauricio, e Iosif Vaisman. "shiny-pred: a server for the prediction of protein disordered regions". F1000Research 8 (28 de fevereiro de 2019): 230. http://dx.doi.org/10.12688/f1000research.17669.1.
Texto completo da fonteBernardo, Nicholas D., e Gretchen A. Macht. "Learning ‘Learning Curves’ with R Shiny". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, n.º 1 (setembro de 2021): 1567–71. http://dx.doi.org/10.1177/1071181321651119.
Texto completo da fonteMarusich, Laura R., e Jonathan Z. Bakdash. "rmcorrShiny: A web and standalone application for repeated measures correlation". F1000Research 10 (8 de novembro de 2021): 697. http://dx.doi.org/10.12688/f1000research.55027.2.
Texto completo da fonteMarusich, Laura R., e Jonathan Z. Bakdash. "rmcorrShiny: A web and standalone application for repeated measures correlation". F1000Research 10 (30 de julho de 2021): 697. http://dx.doi.org/10.12688/f1000research.55027.1.
Texto completo da fonteTeses / dissertações sobre o assunto "Application Shiny"
Biesiada, Jacek. "Shiny Application for Enrichment and Topological Pathway Analysis". University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595846446399689.
Texto completo da fonteNicvert, Lisa. "Méthodes statistiques et outils logiciels pour l'analyse et l'inférence de réseaux écologiques et le traitement de données multi-espèces". Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10130.
Texto completo da fonteInteractions between species in ecological communities are complex: many species can interact with each other in a variety of ways and at different spatial and temporal scales. Moreover, these interaction networks are the result of multiple causes, generate multiple processes and can have indirect effects transmitted through the structure of the network. This complexity calls for a variety of approaches to understand the determinants of interactions and predict their effects in ecological systems. This thesis studies several aspects of ecological interaction networks using a methodological approach that focuses on the description, evaluation and development of statistical methods and software tools. In a first part, I study causes of the structure of interaction networks, focusing on interaction niches and using the notion of trait matching between species. To this end, I use methods from the correspondence analysis family and apply and extend reciprocal scaling methods to the analysis of bipartite networks. I apply these methods to the analysis of a plant-frugivore interaction network in a Peruvian montane forest, and show that species traits can be related to their niche width. In a second part, I study the consequences of interactions through their influence on the spatio-temporal distribution of species. To this end, I use multivariate Hawkes processes to analyze camera trap data. I illustrate these models on five mammals from the South African savanna, showing attraction and avoidance between several of these species at a short spatio-temporal scale. In a third part, I consider camera trap data analysis. I develop a R package to clean and standardize camera trap data intended for the Snapshot Safari program, as well as a Shiny application intended for a more general use to visualize data in an interactive and reproducible way. This thesis presents statistical methods and software tools to analyze complex ecological data and improve our understanding of interaction networks. These results open new perspectives on ecological data analysis and methodological development in ecology
Ankrah, Stephanie. "Protective materials for sporting applications : football shin guards". Thesis, University of Birmingham, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288869.
Texto completo da fonteSertori, Matteo. "Studio e realizzazione dell'interfaccia grafica per un applicativo Health". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21539/.
Texto completo da fonteMukherjee, Sapna Shukla [Verfasser], Rainer [Gutachter] Heintzmann e Jer-Shing [Gutachter] Hunag. "Novel applications of nanogratings for high resolution microscopy / Sapna Shukla Mukherjee ; Gutachter: Rainer Heintzmann, Jer-Shing Hunag". Jena : Friedrich-Schiller-Universität Jena, 2017. http://d-nb.info/1177595753/34.
Texto completo da fonteDenecker, Thomas. "Bioinformatique et analyse de données multiomiques : principes et applications chez les levures pathogènes Candida glabrata et Candida albicans Functional networks of co-expressed genes to explore iron homeostasis processes in the pathogenic yeast Candida glabrata Efficient, quick and easy-to-use DNA replication timing analysis with START-R suite FAIR_Bioinfo: a turnkey training course and protocol for reproducible computational biology Label-free quantitative proteomics in Candida yeast species: technical and biological replicates to assess data reproducibility Rendre ses projets R plus accessibles grâce à Shiny Pixel: a content management platform for quantitative omics data Empowering the detection of ChIP-seq "basic peaks" (bPeaks) in small eukaryotic genomes with a web user-interactive interface A hypothesis-driven approach identifies CDK4 and CDK6 inhibitors as candidate drugs for treatments of adrenocortical carcinomas Characterization of the replication timing program of 6 human model cell lines". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL010.
Texto completo da fonteBiological research is changing. First, studies are often based on quantitative experimental approaches. The analysis and the interpretation of the obtained results thus need computer science and statistics. Also, together with studies focused on isolated biological objects, high throughput experimental technologies allow to capture the functioning of biological systems (identification of components as well as the interactions between them). Very large amounts of data are also available in public databases, freely reusable to solve new open questions. Finally, the data in biological research are heterogeneous (digital data, texts, images, biological sequences, etc.) and stored on multiple supports (paper or digital). Thus, "data analysis" has gradually emerged as a key research issue, and in only ten years, the field of "Bioinformatics" has been significantly changed. Having a large amount of data to answer a biological question is often not the main challenge. The real challenge is the ability of researchers to convert the data into information and then into knowledge. In this context, several biological research projects were addressed in this thesis. The first concerns the study of iron homeostasis in the pathogenic yeast Candida glabrata. The second concerns the systematic investigation of post-translational modifications of proteins in the pathogenic yeast Candida albicans. In these two projects, omics data were used: transcriptomics and proteomics. Appropriate bioinformatics and analysis tools were developed, leading to the emergence of new research hypotheses. Particular and constant attention has also been paid to the question of data reproducibility and sharing of results with the scientific community
LEE, CHENG-WEI, e 李政葦. "An Interactive Web Application of Data Science with R using Shiny". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/469wk9.
Texto completo da fonte國立高雄大學
統計學研究所
107
This study uses the Shiny package in the R programming language to create an interactive web application for data science including time series, data mining, machine learning, regression analysis, etc. Since the Shiny package is a dynamic and interactive application that allows users to change settings and check results right away, it saves users a lot of time from rerunning and modifying programs. In addition, this interactive web application is very helpful for the introduction and further understanding of statistical methods. Users can describe the data through interactive methods and use R programming language to complete all operations, which is helpful for improving the effectiveness of data science in teaching and learning.
Suleman, Nazira. "Mechanism-Based Approach to the Economic Evaluation of Pharmaceuticals". Master's thesis, 2018. http://hdl.handle.net/10451/40032.
Texto completo da fonteA farmacoeconomia é uma disciplina que avalia o uso de medicamentos em termos de recursos na maximização da saúde da população. Dado que os recursos para os cuidados de saúde são finitos, a avaliação económica envolve a estimativa do custo de oportunidade, i.e., os benefícios marginais perdidos como resultado do deslocamento de tratamentos ou serviços existentes para financiar novos medicamentos. A farmacocinética é a ciência que visa o estudo do movimento de fármacos no organismo, o que inclui a absorção, distribuição, metabolismo e eliminação destes e seus metabolitos. Com o advento da química analítica e métodos de quantificação sofisticados, bem como de um aumento do poder de computação, a farmacocinética como ciência tem tido um desenvolvimento exponencial. Uma das áreas da farmacocinética que se tem desenvolvido mais é a farmacocinética populacional: apesar da farmacocinética de um fármaco poder ser estudada individualmente em cada indivíduo, a abordagem populacional é benéfica para o estudo de grupos de pacientes que são difíceis de investigar, como a população de bebés prematuros, pacientes com insuficiência hepática ou renal. Na farmacocinética populacional, cada indivíduo é avaliado simultaneamente com o modelo de efeitos mistos não-lineares (parametrização). Não linear significa que a variável dependente dessa concentração está relacionada não linearmente à associação de variáveis independentes e parâmetros do modelo. Efeitos fixos refere-se aos parâmetros que não se alteram em indivíduos, enquanto o efeito aleatório se refere àqueles parâmetros que se alteram através dos indivíduos. O principal objetivo das estimativas de modelação farmacocinética populacional é o de procurar os parâmetros de farmacocinética populacional e fonte de variabilidade. Os objetivos restantes consistem em concentrações observadas da dose administrada pela deteção das covariáveis preditivas na população avaliada. Em farmacocinética populacional, os indivíduos poderão apenas fornecer dados de concentração plasmática escassos. As cinco principais partes fundamentais para a construção de um modelo farmacocinético populacional incluem: dados, modelo estrutural, modelo estatístico, modelo de covariáveis e software de modelação. Os modelos estruturais definem o perfil de concentração plasmática ao longo do tempo nos indivíduos. Os modelos estatísticos descrevem a variabilidade aleatória na população que não é explicável (como a variabilidade entre as ocasiões), entre a variabilidade do indivíduo ou a variabilidade residual. Os modelos de covariável demonstram a variabilidade estimada pelas características da população, como covariáveis. O software de modelação, como o software de modelação de efeitos mistos não linear, permite a combinação de dados e modelos e aplica o método de estimativa para avaliar parâmetros para os modelos estatísticos, estruturais e de covariáveis que definem os dados. Na modelação farmacocinética populacional, o software possui um algoritmo de minimização do valor da função objetivo, praticando a estimativa de máxima verossimilhança. No momento da adaptação dos dados populacionais, a concentração estimada para cada indivíduo é influenciada pela variância dos parâmetros populacionais e de cada parâmetro individual, e pela variação em cada valor das concentrações previstas e observadas. A avaliação da probabilidade marginal depende dos parâmetros de efeito aleatório (η) e efeito fixo da população. Não há existência de solução analítica para verossimilhança marginal. Enquanto buscava a máxima verossimilhança, inúmeras abordagens foram aplicadas para a aproximação da verossimilhança marginal. O FOCE e o LAPLACE são as abordagens mais antigas que estimam a verdadeira verossimilhança com uma função adicional simplificada. O trabalho de dissertação no âmbito do Mestrado em Ciências Biofarmacêuticas teve por objetivo o estabelecimento de ferramentas baseadas em simulação de dados com base em modelos farmacocinéticos populacionais para uma posterior análise farmacoeconómica. Neste trabalho utilizou-se a informação disponível para a combinação fixa de Glecaprevir e Pibrentasvir (Mavyret®), medicamento usado no tratamento do vírus da hepatite C crónica. As simulações foram realizadas utilizando o software R e seu pacote Shiny. O R é uma linguagem para análise de dados de computação estatística e gráfica. A população simulada no modelo foi agrupada de acordo com as covariáveis similares, sendo simulados 1000 indivíduos por cenário. O relatório de submissão da FDA do Mavyret® foi usado como referência na modelação farmacocinética populacional. Neste relatório encontra-se descrito o modelo farmacocinético populacional desenvolvido, com base nos estudos clínicos realizados para o medicamento. No modelo descrito, foram identificadas diferentes covariáveis. O modelo descrito foi então implementado no software R e o impacto das covariáveis foi estudado com a aplicação Shiny. A população observada foi categorizada em diferentes grupos, tais como doentes tratados com Glecaprevir / Pibrentasvir com compromisso renal e doentes com compromisso renal e cirrose. Foram criados modelos individuais para cada um dos grupos e a comparação entre cada grupo e seus perfis de concentração-tempo foi realizada pelo uso do navegador R e Shiny, onde a atualização nos resultados pode ser vista automaticamente com a alteração em qualquer da covariável ou da variável. Para os diferentes modelos finais incorporados no software e para a população simulada, foram calculados os parâmetros farmacocinéticos AUC e Cmax para posterior análise estatística descritiva. Apesar da implementação dos modelos farmacocinéticos populacionais ter sido realizada em R e Shiny, e os dados terem sido simulados para os diferentes cenários populacionais, a aplicação de metodologias farmacoeconómicas não foram realizadas.
Pharmacoeconomics is the discipline concerned with optimal allocation of resources to maximize population health from the use of medicines. Given that resources for health care are finite, economic evaluation involves estimation of the opportunity cost, that is, the marginal benefits forgone as a result of displacing existing treatments or services to fund new medicines. The purpose of this study is to use tools in pharmacoeconomic analysis for the examination of the positive and adverse impact of the fixed dose combination of Glecaprevir and Pibrentasvir (Mavyret®), used to treat chronic hepatitis C virus. In order to examine the effects in pharmacoeconomics analysis, a population pharmacokinetic model was developed using R software and its package Shiny, where R is a language for data analysis of statistical computing and graphics. The population simulated in the model was grouped according to the similar covariates with the number (n) of 1000. FDA submission report for Mavyret® was used as reference regarding population pharmacokinetics modelling, developed based on the clinical studies performed for the drug product. In the described model, different covariates were identified. The described model was implemented in the R software and the impact of covariates wwas studied with Shiny application. The population observed was categorized in different groups such as patients treated with Glecaprevir/Pibrentasvir having renal impairment and patients with renal impairment and Cirrhosis. Individual models were created for each of the groups and the comparison between each group and their concentration-time profiles was observed that was made easier by the use of R and Shiny web browser where the update in results can be seen spontaneously with the change in any of the covariate or the variable. Different final models were produced and for the simulated population, the pharmacokinetic parameters AUC and Cmax were calculated for descriptive statistical analysis. Despite the implementation of population pharmacokinetics models has been accomplished in R and Shiny, and data has been simulated for different population scenarios, pharmacoeconomic modelling and application of pharmacoeconomic methodologies was not practised.
Chen, Szu-Chuan, e 陳思全. "Green villageSynbiotic eco-village planning-An Application for Jubei and Chiunglin City Shin-Chu county". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/99539516183390894640.
Texto completo da fonte中原大學
建築研究所
90
After the industry revolution ,the human being always hold a concept of environment that man will triumph over nature for a long time .And it makes the main stream value of economic trading system keep overriding the care of ecological environment. In Taiwan,our economics grows rapidly and the industrial business property are developed vigorous from the beginning of R.O.C.. Under the background of the whole times ,the changes of ecological environment also replaced from nature to artificial, and developed behavior replaced the current environment ,the pounce on nature again after being beaten off step by step .People still haven’t be awaken by the importance of paragenesis ecological until now. The research is focus on the 1250 hectare land development case of Pu-Yu plan and the community planner system in Hsin-Chu county. They use the concept of gather village to do a hypothesize operation in specified extent and they try to find a starting point from the original power of community construction to connect with large scale of work out system. Different from the past government plan assignment, they expect to build a plan system based on the life of resident and paragenesis ecological under the current background construction. The research background are mainly divided into three premise of research to do a deep analysis : residential life, paragenesis ecological and the truely locality. The first part of research contents is using the research one himself to record local ”people” pattern in practical participated observation way and try to provide the imagination of large scale plan construction to build the follow-up operation system .The second part is using the hypothesized plan way to operate the countryside land by the station of current Pu-Yu plan. Furthermore, they use the community operation mode by community planner to discuss the real practical possibilities. The final submission part is bring tactical operation method into the whole plan and develop the ecological environment value of new era emphatically ,creating a paragenesis ecological by nature ecology and humane social system.
LIN, YUAN-WEN, e 林淵文. "The application of multivariate statistical method in water quality for the basin of shin-men reservoir". Thesis, 1992. http://ndltd.ncl.edu.tw/handle/24370936631239640637.
Texto completo da fonteLivros sobre o assunto "Application Shiny"
Ankrah, Stephanie. Protective materials for sporting applications - football shin guards. Birmingham: University of Birmingham, 2002.
Encontre o texto completo da fonteResnizky, Hernan G. Learning Shiny. Packt Publishing, 2015.
Encontre o texto completo da fonteWeb Application Development with R Using Shiny. Packt Publishing, 2013.
Encontre o texto completo da fonteWeb Application Development with R Using Shiny. Packt Publishing, Limited, 2013.
Encontre o texto completo da fonteWeb Application Development with R Using Shiny - Second Edition: Integrate the power of R with the simplicity of Shiny to deliver cutting-edge analytics over the Web. Packt Publishing, 2016.
Encontre o texto completo da fonteToolkit for Weighting and Analysis of Nonequivalent Groups: A Tutorial on the TWANG Shiny Application for Time-Varying Treatments. RAND Corporation, 2021. http://dx.doi.org/10.7249/tla570-4.
Texto completo da fonteGriffin, Beth Ann, Chuck Stelzner, Ricardo Sanchez, Matthew Cefalu e Daniel McCaffrey. Toolkit for Weighting and Analysis of Nonequivalent Groups: A Tutorial on the TWANG Shiny Application for Three or More Treatment Groups. RAND Corporation, 2020. http://dx.doi.org/10.7249/tla570-1.
Texto completo da fonteBeeley, Chris, e Shitalkumar R. Sukhdeve. Web Application Development with R Using Shiny: Build stunning graphics and interactive data visualizations to deliver cutting-edge analytics, 3rd Edition. Packt Publishing, 2018.
Encontre o texto completo da fonteEngineering Production-Grade Shiny Apps. Taylor & Francis Group, 2021.
Encontre o texto completo da fonteFay, Colin, S�bastien Rochette, Vincent Guyader e Cervan Girard. Engineering Production-Grade Shiny Apps. Taylor & Francis Group, 2021.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Application Shiny"
Granjon, David. "Web application concepts". In Outstanding User Interfaces with Shiny, 65–75. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003124924-5.
Texto completo da fonteFay, Colin, Sébastien Rochette, Vincent Guyader e Cervan Girard. "Common Application Caveats". In Engineering Production-Grade Shiny Apps, 239–66. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003029878-22.
Texto completo da fonteFay, Colin, Sébastien Rochette, Vincent Guyader e Cervan Girard. "Deploy Your Application". In Engineering Production-Grade Shiny Apps, 207–12. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003029878-19.
Texto completo da fonteAlves, Samuel, Ana Cristina Braga e Rosete Nogueira. "Percentile Growth Curves for Placenta Measures: A Dynamic Shiny Application". In Computational Science and Its Applications – ICCSA 2022 Workshops, 543–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10536-4_36.
Texto completo da fonteAlbert, Jim, Max Marchi e Benjamin S. Baumer. "Using Shiny for Baseball Applications". In Analyzing Baseball Data with R, 334–47. 3a ed. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781032668239-15.
Texto completo da fonteBaker, Katie, Gordon Stephen, Shona Strachan, Miles Armstrong e Ingo Hein. "BLASTmap: A Shiny-Based Application to Visualize BLAST Results as Interactive Heat Maps and a Tool to Design Gene-Specific Baits for Bespoke Target Enrichment Sequencing". In Methods in Molecular Biology, 199–206. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8724-5_14.
Texto completo da fonteAntunes, Ana Rita, e Ana Cristina Braga. "Shiny App to Predict Agricultural Tire Dimensions". In Computational Science and Its Applications – ICCSA 2020, 247–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58808-3_19.
Texto completo da fonteLiu, Junjie, Yuhui Deng e Xiaoling Peng. "Online Statistics Teaching-Assisted Platform with Interactive Web Applications Using R Shiny". In Emerging Technologies for Education, 84–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92836-0_8.
Texto completo da fonteYoshida, Sen, Koji Kamei, Takeshi Ohguro, Kazuhiro Kuwabara e Kaname Funakoshi. "Building a Network Community Support System on the Multi-agent Platform Shine". In Design and Applications of Intelligent Agents, 88–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44594-3_7.
Texto completo da fonteRodrigues, Claudia, Ana Rita Antunes e Ana Cristina Braga. "Shiny App to Predict the Risk of Death in Very Low Birth Weight Newborns Through a New Classifier". In Computational Science and Its Applications – ICCSA 2021, 593–608. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86973-1_42.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Application Shiny"
Anggono, W. S. P. Dwi, Kristoko Dwi Hartomo e Eko Sediyono. "Standardized precipitation index web application mapping shiny model". In 2017 International Conference on Innovative and Creative Information Technology (ICITech). IEEE, 2017. http://dx.doi.org/10.1109/innocit.2017.8319131.
Texto completo da fonteSatyahadewi, Neva, e Hendra Perdana. "Web Application Development for Inferential Statistics using R Shiny". In 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210508.099.
Texto completo da fonteScrivner, Olga, Vinita Chakilam, Nilima Sahoo e Stephan De Spiegeleire. "Topic Analysis through Streamgraph via Shiny Application: A Social Collaborative Approach". In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2018. http://dx.doi.org/10.24251/hicss.2018.025.
Texto completo da fonteKonda, Kazuki, e Yoshiro Yamamoto. "Analyze the trends of customer purchase data and visualize by the Shiny application". In 2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, 2018. http://dx.doi.org/10.1109/ictke.2018.8612310.
Texto completo da fonteBani, Ryota, e Yoshiro Yamamoto. "Development of an R-Shiny-based Shooting Area Visualization Application for Use in Basketball". In 2022 20th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, 2022. http://dx.doi.org/10.1109/ictke55848.2022.9983260.
Texto completo da fonteMurillo, Danny, Dalys Saavedra e Robinson Zapata. "Web application in Shiny for the extraction of data from profiles in Google Scholar". In 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions”. Latin American and Caribbean Consortium of Engineering Institutions, 2022. http://dx.doi.org/10.18687/laccei2022.1.1.235.
Texto completo da fonteHarding, Kevin G. "Measuring shiny surfaces with optical gages". In Dimensional Optical Metrology and Inspection for Practical Applications XIII, editado por Song Zhang, Kevin G. Harding, Andrés G. Marrugo, Beiwen Li e Jae-Sang Hyun. SPIE, 2024. http://dx.doi.org/10.1117/12.3013004.
Texto completo da fonteZharri, Elona, e Stefan A. Robila. "Shiny Dashboard - NYC Trees Benefit Estimation". In 2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT). IEEE, 2022. http://dx.doi.org/10.1109/lisat50122.2022.9923953.
Texto completo da fonteCrossett, Jonathan, Jing Qi, William Cowen, Elijah Gagne e Christian Darabos. "Shiny Concurrency: Scaling Single-Threaded and Resource Intensive Applications". In PEARC '24: Practice and Experience in Advanced Research Computing. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3626203.3670590.
Texto completo da fonteMorgan, Jeffrey J., Otto C. Wilson e Prahlad G. Menon. "The Wisdom of Crowds Approach to Influenza-Rate Forecasting". In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86559.
Texto completo da fonteRelatórios de organizações sobre o assunto "Application Shiny"
Mazorchuk, Mariia S., Tetyana S. Vakulenko, Anna O. Bychko, Olena H. Kuzminska e Oleksandr V. Prokhorov. Cloud technologies and learning analytics: web application for PISA results analysis and visualization. [б. в.], junho de 2021. http://dx.doi.org/10.31812/123456789/4451.
Texto completo da fonteArmstrong, Dave. Interactive Dashboards and Web Apps with Shiny in R. Instats Inc., 2023. http://dx.doi.org/10.61700/3g41zs4gcnd3e469.
Texto completo da fonteArmstrong, Dave. Interactive Dashboards and Web Apps with Shiny in R. Instats Inc., 2023. http://dx.doi.org/10.61700/b3vv6dox5eqdl469.
Texto completo da fonteIhaddaden, Fodil. Introduction to Web App Development with R Shiny. Instats Inc., 2024. http://dx.doi.org/10.61700/c3vssh3mivcmo1636.
Texto completo da fonte