Royden_Real_Analysis_Solutions
Ordyl SY 300商品说明书
PRODUCT DESCRIPTIONOrdyl SY 300 is a solvent type permanent dry film for special MEMS applications.The Ordyl SY 300 in connection with his auxiliary product line CFC free: Ordyl SY Developer and Ordyl SY Rinse, offer the following performances:∙Excellent resolution ∙Excellent heat resistance ∙Excellent chemical resistance ∙High stability ∙BiocompatibilityOrdyl SY 300 could be used for sealing application, due to the capability to be pressed together with a top plate.SY 355Main Features: -Excellent chemical resistance -Biocompatibility Typical Application:-MEMS-Sealing applicationAvailable Thickness:-10 µm (0.4 mils), 20 µm (0.8 mils), 30 µm (1.2 mils), 55 µm (2.2 mils) and 125 µm (5 mils) -Different thickness available on requestORDYL SY 300PRODUCT DATA SHEETEdition 08 –28 August 2019PROCESS INFORMATIONOrdyl SY 300 guarantee good adhesion on the following surface:-Glass-Silicium-Kapton-MylarWe recommend good surface cleaning in order to obtain optimal performance.LaminationPanels must be thoroughly dry prior to lamination.MANUAL LAMINATOR AUTOMATIC LAMINATOR Pre-heat (OPTIONAL) (OPTIONAL) Hot roll temperature 105 – 125°C (221 – 257°F) 105 – 125°C (221 – 257°F)Lamination roll pressure 2.5 – 3.5 bar (36 – 50 Psi) 2.5 – 6.0 bar (36 – 87 Psi) Lamination speed 1 – 3m/min (3 – 10 feet/min) 1 – 3m/min (3 – 10 feet/min)Seal temperature --- 40 – 80°C (104 – 176°F)Seal pressure --- 3.0 – 6.0 bar (44 – 87 Psi)Seal time --- 1 – 4 sec.Post lamination Hold TimeWe recommend a hold time of at least 15 min, or in any case the minimum hold time necessary to allow panels to cool down to room temperature.Hold time should not be over 1 week.ExposureWe recommend using UV lamps or laser source with emission peak at 360 – 380 nm.Energy (mJ/cm2) 100 150 200 250 300 350 400 SST 21 5 6 7 8 9 9.5 10RST 25 4 7 10 13 16 17-18 19Hold Time after exposureWe recommend a minimum hold time after exposure of at least 15 minutes.DevelopingSY 300 could be develop with spray, paddle or dipping method.Using Ordyl SY Developer in dipping process at room temperature maintain the Break Point between 60% and 80% depend on application.Use Ordyl SY Rinse to remove scum and clean the surface.If final rinse with DI water is necessary an intermediate rinse with IPA is suggested.Post BakeAfter developing is necessary a post-baking at 150°C (302°F) for 30 – 60 min.StrippingOrdyl SY 300 could be stripped only before post bake using Ordyl SY Developer increasing dipping time indicated in “Ordyl SY Developer product data sheet” or using solvent like Acetone or MEK in dipping method.RESIST PROFILEFor the test we used a 55 µm thickness dry film, laminated on SiO2 wafer.EXPOSURE LINE SPACE100 mJ/cm260 µm (2.4 mils) 50 µm (2 mils)150 mJ/cm250 µm (2 mils) 60 µm (2.4 mils)200 mJ/cm240 µm (1.6 mils) 70 µm (2.8 mils)250 mJ/cm240 µm (1.6 mils) 80 µm (3.1 mils) Exposure Unit ORC HMW201B not collimated.REFERENCES:A Lab-On-A-Chip For Automated RNA Extraction From Bacteriahttp://www.freidok.uni-freiburg.de/volltexte/7020/Lab-on-a-Foil: microfluidics on thin and flexible filmsThis critical review is motivated by an increasing interest of the microfluidics community in developing complete Lab-on-a-Chip solutions based on thin and flexible films (Lab-on-a-Foil).Those implementations benefit from a broad range of fabrication methods that are partly adopted from well-established macroscale processes or are completely new and promising. In addition, thin and flexible foils enable various features like low thermal resistance for efficient thermocycling or integration of easily deformable chambers paving the way for new means of on-chip reagent storage or fluid transport. From an economical perspective, Lab-on-a-Foil systems are characterised by low material consumption and often low-cost materials which are attractive for cost-effective high-volume fabrication of self-contained disposable chips. The first part of this review focuses on available materials, fabrication processes and approaches for integration of microfluidic functions including liquid control and transport as well as storage and release of reagents. In the second part, an analysis of the state of Lab-on-a-Foil applications is provided with a special focus on nucleic acid analysis, immunoassays, cell-based assays and home care testing. We conclude that the Lab-on-a-Foil approach is very versatile and significantly expands the toolbox for the development of Lab-on-a-Chip solutions.http://www.loac-imtek.de/fileadmin/loac/Projekte_und_Publikationen/Ausgewaehlte_Publikationen/lab-on-a-foil_-_microfluidics_on_thin_and_flex.pdfDry Film Resist For Fast Fluidic PrototypingDry film photoresist is used for creating microfluidic structures by sandwiching the patterned resist in between of two substrates.The technique is applied for creating hybrid biochips for dielectrophoretic cell manipulation.Multiple level lithography is demonstrated and biocompatibility of the resist is proven.Due to simple fabrication procedures the resist can be processed in a low-tech environment.http://hal.inria.fr/docs/00/34/64/73/PDF/Dry_film_resist_for_fast_fluidic_prototyping.pdfFor any other technical information (storage conditions, packaging information, etc.) refer to the Ordyl Specification (Form EE.P11.CV.02-ww).The information stated in this Data Sheet regarding the use of materials is based upon experience under laboratory controls. Elga Europe makes no guaranty or warranty, express or implied, to such use, handling or possession of such materials, or of the application of any process described in our bulletins of the results sought to be obtained, whether in accordance with the directions or claimed so to be. Any informationor statements contained herein are expressly made subject to the foregoing provisions and the terms and conditions embodied in our invoice covering such materials with are to be deemed part herein. The publication hereof describing any process is not to be deemed not taken as license to operate under, nor recommendation to infringe, any patent.The seller binds itself only to deliver goods in accordance whit the general description upon which they are sold whether or not any special particular description shall have been given or implied by law.Any such special or particular description shall be taken only as the expression of seller’s opinion in that behalf. The seller does not give any warranty as to the quality (save that the goods are of merchantable quality), state condition fitness of the goods or use to which the goods may be put. Claims on account of weight, loss of or damage to the goods in transit ( so far as seller is liable) shall be made in writing to the seller within the period of 30 days of receipt thereof.No claim shall be entertained after the expiration of the appropriate period mentioned above and the seller’s liability by reason of any such claim shall not in any event the purchase price of the goods in respect of which a claim is made. Goods shall not be returned to the seller without the seller’s express written permission.Via Caldara 3 - 20020 Lainate (MI) ItalyE-mail: ********************。
演示文稿数学专业英语第八讲附数学课程英文表达
4、L. Hormander “Linear Partial Differential Operators, ” I&II：偏微分方程的经典参考书； 5、A Course in Abstract Harmonic Analysis by Folland：高级的研究生调和分析教材； 6、Abstract Harmonic Analysis by Ross Hewitt：抽象调和分析的经典参考书； 7、Harmonic Analysis by Elias M. Stein：标准的研究生调和分析教材； 8、Elliptic Partial Differential Equations of Second Order by David Gilbarg：偏微分 方程的经典参考书； 9、Partial Differential Equations ，by Jeffrey Rauch：标准的研究生偏微分方程教材。
覆盖范围较广；
9、Elements of Homotopy Theory by G.W. Whitehead：高级、经典的代数拓扑参考 书。
第六页，共34页。
• 实分析、泛函分析：
1、Royden, Real analysis：标准研究生分析教材； 2、Walter Rudin, Real and complex analysis：标准研究生分析教材；
；
4、Principles of Algebraic Geometry by giffiths/harris：全面、经典的代数几何参考 书，偏复代数几何； 5、Commutative Algebra with a view toward Algebraic Geometry by Eisenbud：高
调代数参考书；
基于粒子滤波算法的方位跟踪方法研究
基于粒子滤波算法的方位跟踪方法研究周彤;宋立臣【摘要】与传统的声压水听器相比,矢量水听器(AVS)可以同时、共点接收水下声场的声压和振速信息,利用单个水听器可以估计出目标的方位(DOA)信息.本文将MUISIC方位估计方法和粒子滤波(Particle Filtering,PF)跟踪方法结合,给出一种高效的水下运动目标的实时方位跟踪方法(PF-MUSIC),仿真对比分析了MUSIC方法和PF-MUISC方法的方位跟踪性能,并通过海上试验验证了跟踪方法的实用性.结果表明:PF-MUSIC法的计算量远小于MUSIC方法,PF-MUSIC方法的误差更小并且跟踪曲线更平滑,适用于对水下目标的实时方位跟踪.【期刊名称】《舰船科学技术》【年(卷),期】2018(040)011【总页数】5页(P134-138)【关键词】粒子滤波;单矢量水听器;方位跟踪【作者】周彤;宋立臣【作者单位】大连测控技术研究所,辽宁大连 116013;大连测控技术研究所,辽宁大连 116013【正文语种】中文【中图分类】TP3910 引言在舰船水下辐射噪声测量的过程中，安全、可靠地引导被测目标通过测量区域，并保证被测目标与测量系统之间的有效距离以满足测试需求是极其重要的，也是一直关心的问题。
现有的被测目标引导方法，往往依靠单一导航信标配合测距系统实现，通常仅提供被测目标与导航信标之间的单点距离信息，缺乏方位信息，使得目标实际航行轨迹与试验要求轨迹有一定的偏差，造成测量单程无效，从而降低了测试效率，延长了试验时间[1]。
目前方位估计算法的通常假设目标在观测时间内为静止状态，即静态方位算法，同时采用大计算量和多快拍数的批处理形式进行方位估计[2]。
然而在实际情况下，目标通常为运动状态，批处理方式的静态方位估计方法不适用于实时连续跟踪。
同时，静态方位估计方法是互不相关地估计2个连续时刻的方位信息，并没有考虑到2个连续时刻状态的关联性，进而造成动态方位估计偏差较大。
219515743_辐射源个体识别的一种可解释性测试架构
第 21 卷 第 6 期2023 年 6 月太赫兹科学与电子信息学报Journal of Terahertz Science and Electronic Information TechnologyVol.21，No.6Jun.，2023辐射源个体识别的一种可解释性测试架构刘文斌a，范平志a，李雨锴b，王钰浩b，孟华*b(西南交通大学 a.信息科学与技术学院；b.数学学院，四川成都611756)摘要：由于射频信号种类多，电磁环境复杂，特征提取难度大，现有的基于人工特征的射频辐射源个体识别方法的鲁棒性、适用性难以满足应用需求。
数据驱动的深度学习方法虽然可以提供更灵活的辐射源个体识别模式，但深度学习方法自身可解释性差，而且缺乏通用测试模式来评价一个深度学习方法的优劣。
本文在电磁大数据非凡挑战赛目标个体数据集的基础上，探索了基于该数据集的深度学习模型测试方法，提出面向辐射源个体识别神经网络模型的通用测试系统架构。
该构架通过信号特征遮掩、生成对抗网络(GAN)、欺骗信号汇集、信道模拟等方法构造仿真测试样本，并把测试样本与原样本数据导入深度模型进行识别结果对比测试。
基于测试结果分析了深度模型聚焦的信号关键特征位置，分析模型的鲁棒性，揭示信道环境对识别性能的影响，从而解释了深度学习网络模型的性能。
关键词：辐射源个体识别；可解释性；生成对抗网络；无线信号欺骗中图分类号：TN92 文献标志码：A doi：10.11805/TKYDA2022243An interpretable testing architecture for specificemitter identificationLIU Wenbin a，FAN Pingzhi a，LI Yukai b，WANG Yuhao b，MENG Hua*b(a.School of Information Science & Technology; b.School of Mathematics, Southwest Jiaotong University, Chengdu Sichuan 611756, China)AbstractAbstract：：Due to the diversity of RF signals, the complexity of the electromagnetic environment, and the difficulty of feature extraction, the robustness and applicability of the existing artificial features-based RF-specific emitter identification methods cannot meet the application requirements. Althoughthe data-driven deep learning methods can provide a more flexible mode of specific emitteridentification, they are less interpretable and lack a general test mode to evaluate their advantages anddisadvantages. An evaluation method is explored for the deep learning model on the target individualdataset of the Electromagnetic Big Data Super Contest, and a general testing system architecture isproposed for the specific emitter identification model based on deep neural networks. The frameworkconstructs the simulation test samples through signal feature masking, Generative Adversarial Network(GAN), deception signal collection, channel simulation and other methods, and imports the test samplesand original data into the deep model to compare the recognition results. The test results are employed tojudge the location of the signal key features extracted by the deep model, to analyze the robustness of themodel, and to reveal the impact of the channel environment on the recognition performance, thus theperformance of the deep learning model can be interpretable.KeywordsKeywords：：specific emitter identification；interpretability；Generative Adversarial Network(GAN)；wireless signal spoofing信号识别包括信号检测、信号类型识别、辐射源个体识别(Specific Emitter Identification，SEI)3个层次[1]。
Rietveld方法原理.ppt
Structural model
Raw data
shift
Rietveld Refinement
Refined model
How RM works?
The RM refines a structure by minimizing a quantity through the Newton-Raphson algorithm
0
1
2
3
4
value of x
4000
3900
3800
3700
3600
3500
3400
0
1 value o2f x
3
4
Phase transition of BaTiO3 from
tetragonal to cubic at about 132˚C
YBa3B3O9: Phase transition and structure determination
Grain size
Atomic Occupancy Incommensurate Structure
Debye Temperatures Structure factors
Crystallinity
Phase transitions
Magnetic structures
……
History Review
• Rietveld originally introduced the Profile Refinement method (Using stepscanned data rather than integrated Powder peak intensity) (1966,1967)
StochasticProcessesRossSolutionsManual-…
Stochastic Processes Ross Solutions ManualIf looking for the book Stochastic processes ross solutions manual in pdf format, then you have come on to the rightsite. We present utter variation of this book in DjVu, txt, PDF, ePub, doc forms. You may reading Robinair model34134z repair manual online stochastic-processes-ross-solutions-manual.pdf or download. Additionally, on our websiteyou may read the guides and another art books online, or downloading their. We wish to invite attention that oursite does not store the eBook itself, but we give ref to site wherever you may download either read online. So ifyou have must to download pdf Stochastic processes ross solutions manual stochastic-processes-ross-solutions-manual.pdf,then you have come on to loyal website. We have Stochastic processes ross solutions manual doc, DjVu, ePub, txt,PDF formats. We will be glad if you go back us afresh.stochastic processes solutions manual to - Get this from a library! Stochastic processes Solutions manual to accompany stochastic processes. [Sheldon Mark Ross]ross stochastic processes solution manual - Description Date Speed Downloads; PROBABILITY AND STOCHASTIC PROCESSES solution manual pdf Download by raj0386sheldon ross stochastic processes solution manual - sheldon ross stochastic processes solution manual at - Download free pdf files,ebooks and documents of sheldon ross stochastic processes solution manualsolutions to sheldon ross stochastic processes | - Sheldon M. Ross, Stochastic Processes, John Wiley and Sons.3. S A First Course in Probability, 8th Edition, Ross, Solutions Manual. A First Course ininstructor solutions manual pdf download ~ - Stochastic processes ross solution manual, instructor's manual to accompany. introduction to. probability models. tenth edition. sheldon m. ross. university ofstochastic processes: an introduction, second - Stochastic Processes: An Introduction, Second Edition Solutions manual available for qualifying instructors . Stochastic Processes in Science,roy d. yates solutions manual probability and - Probability and Stochastic Processes Solutions Manual to accompany Corporate Finance By Stephen A. Ross 6 edition Solutions Manual to accompany Investmentessentials of stochastic processes solution manual - Essentials Of Stochastic Processes Solution Manual Pdf books, ebooks, manuals and documents at EDU Libs. Stochastic Processes Ross Solutions Manual.pdffundamentals of probability with stochastic - Fundamentals of Probability With stochastic processes 3/e (Solutions Manual ) By Saeed Ghahramani A First Course In Probability Solution Manual,Ross 6thsheldon ross, stochastic processes 2nd ed - Stochastic Processes (9780471120629): Sheldon M. Ross: the exercises should come with complete worked solutions. Read more Published on March 9,(2011) sheldon m ross stochastic process 2nd - (2011) Sheldon M Ross Stochastic Process 2nd Edition Solution Manual > /kmp7hp8introduction to stochastic processes - - Sheldon M. Ross. Stochastic processes. Very good book on stochastic processes, An Introduction to the Numerical Solution of Markov Chains.ross stochastic processes solution manual | - Download links for Ross Stochastic Process Solution .doc MSWord Document. Download links for Solution Manual Stochastic Processes Sheldon Ross .doc MSWord Document.ross solution manual stochastic processes - foursquare gospel bible lessons jeep grand cherokee wire context clues module gateway m360 manuals carrier how to get superheat pltw engineer crossword 1.1 amswersprobability - stochastic processes solution - Stochastic Processes Solution manuals. stash of solution manuals for stochastic processes Models by Sheldon M. Ross. It has the 10 edition solution manualstochastic processes ross solution manual - - Document/File: , filesize: n/a. Filetype: PDF. A Poisson process is a simple and widely used stochastic process for modelingstochastic processes solution manual - manuals - Stochastic processes solution manual. (Solution Manual) Sheldon Ross Probability and Stochastic Processes Solutions Manual 255 Problem 9.4.4 Thesesolutions manual _ probability, random variables and - Solutions Manual _ Probability, Random Variables and Stochastic Processes Solutions - Papoulis.2002 - Free ebook download as PDF File (.pdf), Text file solutions manual to accompany stochastic - Get this from a library! Solutions manual to accompany Stochastic processes. [Sheldon M Ross]probability and statistics ser.: stochastic - Probability and Statistics Ser.: Stochastic Processes : Solutions Manual by Sheldon M. Ross (1983, Paperback, Teacher's Edition of Textbook) (Paperback, 1983)probability stochastic processes solution manual | - Stochastic Process Ross Solution Manual Stochastic Processes Ross Solutions Manual | Probability And Stochastic Processes Solutions Manual rapidshare Related PDFs:principles of accounting 101 reeves solutions manual, 1995 polaris sportsman 400 service manual, peugeot 207 manual english, case ih rbx 562 operators manual, jsc lecture guide, carrier ac wall remote control manual model 68rv1112a, generac 15000 generator manual, cub cadet 1220 hydro manual, modern biology study guide answers chapter17, entrepreneurship hisrich 8th edition study guide, study guide for electrician apprentice eti, haas vf oe manual, 1996 yamaha 1300cc royal star manual, dibal l series service manual, 2015 vw jetta sportswagen tdi repair manual, mariner 15 hk 2015 manual, gilera runner 50 poggiali manual, pyxis guide console, fanuc powermate parameter manual, quiznos sandwich make guide, daisy 4500 manual, kenworth parts manual k100,p90x owners manual, yamaha it 200 service manual, quantitative chemical analysis 7th edition solution manual, kasea mighty mite 50 service manual, tennessee kindergarten pacing guide, ford ranger gearbox repair manual, 1987 1997 kawasaki zx600 750 service repair manual download, massey ferguson 699 service manual, wood badge training manual, troy bilt lawn mower repair manuals, kubota b7100 hst manual, maths golden guide of class 9 ncert, manual for 4360 ford diesel tractor, 1998 mercedes ml320 owners manual, nissan 15 electric forklift service manual, mechanical engineering design solutions manual 9th edition, carrier 58pav070 12 manual, weed eater ge21 repair manual。
Instructional_design
Instructional designFrom Wikipedia, the free encyclopediaInstructional Design(also called Instructional Systems Design (ISD)) is the practice of maximizing the effectiveness, efficiency and appeal of instruction and other learning experiences. The process consists broadly of determining the current state and needs of the learner, defining the end goal of instruction, and creating some "intervention" to assist in the transition. Ideally the process is informed by pedagogically(process of teaching) and andragogically(adult learning) tested theories of learning and may take place in student-only, teacher-led or community-based settings. The outcome of this instruction may be directly observable and scientifically measured or completely hidden and assumed. There are many instructional design models but many are based on the ADDIE model with the five phases: 1) analysis, 2) design, 3) development, 4) implementation, and 5) evaluation. As a field, instructional design is historically and traditionally rooted in cognitive and behavioral psychology.HistoryMuch of the foundations of the field of instructional design was laid in World War II, when the U.S. military faced the need to rapidly train large numbers of people to perform complex technical tasks, fromfield-stripping a carbine to navigating across the ocean to building a bomber—see "Training Within Industry(TWI)". Drawing on the research and theories of B.F. Skinner on operant conditioning, training programs focused on observable behaviors. Tasks were broken down into subtasks, and each subtask treated as a separate learning goal. Training was designed to reward correct performance and remediate incorrect performance. Mastery was assumed to be possible for every learner, given enough repetition and feedback. After the war, the success of the wartime training model was replicated in business and industrial training, and to a lesser extent in the primary and secondary classroom. The approach is still common in the U.S. military.[1]In 1956, a committee led by Benjamin Bloom published an influential taxonomy of what he termed the three domains of learning: Cognitive(what one knows or thinks), Psychomotor (what one does, physically) and Affective (what one feels, or what attitudes one has). These taxonomies still influence the design of instruction.[2]During the latter half of the 20th century, learning theories began to be influenced by the growth of digital computers.In the 1970s, many instructional design theorists began to adopt an information-processing-based approach to the design of instruction. David Merrill for instance developed Component Display Theory (CDT), which concentrates on the means of presenting instructional materials (presentation techniques).[3]Later in the 1980s and throughout the 1990s cognitive load theory began to find empirical support for a variety of presentation techniques.[4]Cognitive load theory and the design of instructionCognitive load theory developed out of several empirical studies of learners, as they interacted with instructional materials.[5]Sweller and his associates began to measure the effects of working memory load, and found that the format of instructional materials has a direct effect on the performance of the learners using those materials.[6][7][8]While the media debates of the 1990s focused on the influences of media on learning, cognitive load effects were being documented in several journals. Rather than attempting to substantiate the use of media, these cognitive load learning effects provided an empirical basis for the use of instructional strategies. Mayer asked the instructional design community to reassess the media debate, to refocus their attention on what was most important: learning.[9]By the mid- to late-1990s, Sweller and his associates had discovered several learning effects related to cognitive load and the design of instruction (e.g. the split attention effect, redundancy effect, and the worked-example effect). Later, other researchers like Richard Mayer began to attribute learning effects to cognitive load.[9] Mayer and his associates soon developed a Cognitive Theory of MultimediaLearning.[10][11][12]In the past decade, cognitive load theory has begun to be internationally accepted[13]and begun to revolutionize how practitioners of instructional design view instruction. Recently, human performance experts have even taken notice of cognitive load theory, and have begun to promote this theory base as the science of instruction, with instructional designers as the practitioners of this field.[14]Finally Clark, Nguyen and Sweller[15]published a textbook describing how Instructional Designers can promote efficient learning using evidence-based guidelines of cognitive load theory.Instructional Designers use various instructional strategies to reduce cognitive load. For example, they think that the onscreen text should not be more than 150 words or the text should be presented in small meaningful chunks.[citation needed] The designers also use auditory and visual methods to communicate information to the learner.Learning designThe concept of learning design arrived in the literature of technology for education in the late nineties and early 2000s [16] with the idea that "designers and instructors need to choose for themselves the best mixture of behaviourist and constructivist learning experiences for their online courses" [17]. But the concept of learning design is probably as old as the concept of teaching. Learning design might be defined as "the description of the teaching-learning process that takes place in a unit of learning (eg, a course, a lesson or any other designed learning event)" [18].As summarized by Britain[19], learning design may be associated with:∙The concept of learning design∙The implementation of the concept made by learning design specifications like PALO, IMS Learning Design[20], LDL, SLD 2.0, etc... ∙The technical realisations around the implementation of the concept like TELOS, RELOAD LD-Author, etc...Instructional design modelsADDIE processPerhaps the most common model used for creating instructional materials is the ADDIE Process. This acronym stands for the 5 phases contained in the model:∙Analyze– analyze learner characteristics, task to be learned, etc.Identify Instructional Goals, Conduct Instructional Analysis, Analyze Learners and Contexts∙Design– develop learning objectives, choose an instructional approachWrite Performance Objectives, Develop Assessment Instruments, Develop Instructional Strategy∙Develop– create instructional or training materialsDesign and selection of materials appropriate for learning activity, Design and Conduct Formative Evaluation∙Implement– deliver or distribute the instructional materials ∙Evaluate– make sure the materials achieved the desired goals Design and Conduct Summative EvaluationMost of the current instructional design models are variations of the ADDIE process.[21] Dick,W.O,.Carey, L.,&Carey, J.O.(2004)Systematic Design of Instruction. Boston,MA:Allyn&Bacon.Rapid prototypingA sometimes utilized adaptation to the ADDIE model is in a practice known as rapid prototyping.Proponents suggest that through an iterative process the verification of the design documents saves time and money by catching problems while they are still easy to fix. This approach is not novel to the design of instruction, but appears in many design-related domains including software design, architecture, transportation planning, product development, message design, user experience design, etc.[21][22][23]In fact, some proponents of design prototyping assert that a sophisticated understanding of a problem is incomplete without creating and evaluating some type of prototype, regardless of the analysis rigor that may have been applied up front.[24] In other words, up-front analysis is rarely sufficient to allow one to confidently select an instructional model. For this reason many traditional methods of instructional design are beginning to be seen as incomplete, naive, and even counter-productive.[25]However, some consider rapid prototyping to be a somewhat simplistic type of model. As this argument goes, at the heart of Instructional Design is the analysis phase. After you thoroughly conduct the analysis—you can then choose a model based on your findings. That is the area where mostpeople get snagged—they simply do not do a thorough-enough analysis. (Part of Article By Chris Bressi on LinkedIn)Dick and CareyAnother well-known instructional design model is The Dick and Carey Systems Approach Model.[26] The model was originally published in 1978 by Walter Dick and Lou Carey in their book entitled The Systematic Design of Instruction.Dick and Carey made a significant contribution to the instructional design field by championing a systems view of instruction as opposed to viewing instruction as a sum of isolated parts. The model addresses instruction as an entire system, focusing on the interrelationship between context, content, learning and instruction. According to Dick and Carey, "Components such as the instructor, learners, materials, instructional activities, delivery system, and learning and performance environments interact with each other and work together to bring about the desired student learning outcomes".[26] The components of the Systems Approach Model, also known as the Dick and Carey Model, are as follows:∙Identify Instructional Goal(s): goal statement describes a skill, knowledge or attitude(SKA) that a learner will be expected to acquire ∙Conduct Instructional Analysis: Identify what a learner must recall and identify what learner must be able to do to perform particular task ∙Analyze Learners and Contexts: General characteristic of the target audience, Characteristic directly related to the skill to be taught, Analysis of Performance Setting, Analysis of Learning Setting∙Write Performance Objectives: Objectives consists of a description of the behavior, the condition and criteria. The component of anobjective that describes the criteria that will be used to judge the learner's performance.∙Develop Assessment Instruments: Purpose of entry behavior testing, purpose of pretesting, purpose of posttesting, purpose of practive items/practive problems∙Develop Instructional Strategy: Pre-instructional activities, content presentation, Learner participation, assessment∙Develop and Select Instructional Materials∙Design and Conduct Formative Evaluation of Instruction: Designer try to identify areas of the instructional materials that are in need to improvement.∙Revise Instruction: To identify poor test items and to identify poor instruction∙Design and Conduct Summative EvaluationWith this model, components are executed iteratively and in parallel rather than linearly.[26]/akteacher/dick-cary-instructional-design-mo delInstructional Development Learning System (IDLS)Another instructional design model is the Instructional Development Learning System (IDLS).[27] The model was originally published in 1970 by Peter J. Esseff, PhD and Mary Sullivan Esseff, PhD in their book entitled IDLS—Pro Trainer 1: How to Design, Develop, and Validate Instructional Materials.[28]Peter (1968) & Mary (1972) Esseff both received their doctorates in Educational Technology from the Catholic University of America under the mentorship of Dr. Gabriel Ofiesh, a Founding Father of the Military Model mentioned above. Esseff and Esseff contributed synthesized existing theories to develop their approach to systematic design, "Instructional Development Learning System" (IDLS).The components of the IDLS Model are:∙Design a Task Analysis∙Develop Criterion Tests and Performance Measures∙Develop Interactive Instructional Materials∙Validate the Interactive Instructional MaterialsOther modelsSome other useful models of instructional design include: the Smith/Ragan Model, the Morrison/Ross/Kemp Model and the OAR model , as well as, Wiggins theory of backward design .Learning theories also play an important role in the design ofinstructional materials. Theories such as behaviorism , constructivism , social learning and cognitivism help shape and define the outcome of instructional materials.Influential researchers and theoristsThe lists in this article may contain items that are not notable , not encyclopedic , or not helpful . Please help out by removing such elements and incorporating appropriate items into the main body of the article. (December 2010)Alphabetic by last name∙ Bloom, Benjamin – Taxonomies of the cognitive, affective, and psychomotor domains – 1955 ∙Bonk, Curtis – Blended learning – 2000s ∙ Bransford, John D. – How People Learn: Bridging Research and Practice – 1999 ∙ Bruner, Jerome – Constructivism ∙Carr-Chellman, Alison – Instructional Design for Teachers ID4T -2010 ∙Carey, L. – "The Systematic Design of Instruction" ∙Clark, Richard – Clark-Kosma "Media vs Methods debate", "Guidance" debate . ∙Clark, Ruth – Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load / Guided Instruction / Cognitive Load Theory ∙Dick, W. – "The Systematic Design of Instruction" ∙ Gagné, Robert M. – Nine Events of Instruction (Gagné and Merrill Video Seminar) ∙Heinich, Robert – Instructional Media and the new technologies of instruction 3rd ed. – Educational Technology – 1989 ∙Jonassen, David – problem-solving strategies – 1990s ∙Langdon, Danny G - The Instructional Designs Library: 40 Instructional Designs, Educational Tech. Publications ∙Mager, Robert F. – ABCD model for instructional objectives – 1962 ∙Merrill, M. David - Component Display Theory / Knowledge Objects ∙ Papert, Seymour – Constructionism, LOGO – 1970s ∙ Piaget, Jean – Cognitive development – 1960s∙Piskurich, George – Rapid Instructional Design – 2006∙Simonson, Michael –Instructional Systems and Design via Distance Education – 1980s∙Schank, Roger– Constructivist simulations – 1990s∙Sweller, John - Cognitive load, Worked-example effect, Split-attention effect∙Roberts, Clifton Lee - From Analysis to Design, Practical Applications of ADDIE within the Enterprise - 2011∙Reigeluth, Charles –Elaboration Theory, "Green Books" I, II, and III - 1999-2010∙Skinner, B.F.– Radical Behaviorism, Programed Instruction∙Vygotsky, Lev– Learning as a social activity – 1930s∙Wiley, David– Learning Objects, Open Learning – 2000sSee alsoSince instructional design deals with creating useful instruction and instructional materials, there are many other areas that are related to the field of instructional design.∙educational assessment∙confidence-based learning∙educational animation∙educational psychology∙educational technology∙e-learning∙electronic portfolio∙evaluation∙human–computer interaction∙instructional design context∙instructional technology∙instructional theory∙interaction design∙learning object∙learning science∙m-learning∙multimedia learning∙online education∙instructional design coordinator∙storyboarding∙training∙interdisciplinary teaching∙rapid prototyping∙lesson study∙Understanding by DesignReferences1.^MIL-HDBK-29612/2A Instructional Systems Development/SystemsApproach to Training and Education2.^Bloom's Taxonomy3.^TIP: Theories4.^Lawrence Erlbaum Associates, Inc. - Educational Psychologist -38(1):1 - Citation5.^ Sweller, J. (1988). "Cognitive load during problem solving:Effects on learning". Cognitive Science12 (1): 257–285.doi:10.1016/0364-0213(88)90023-7.6.^ Chandler, P. & Sweller, J. (1991). "Cognitive Load Theory andthe Format of Instruction". Cognition and Instruction8 (4): 293–332.doi:10.1207/s1532690xci0804_2.7.^ Sweller, J., & Cooper, G.A. (1985). "The use of worked examplesas a substitute for problem solving in learning algebra". Cognition and Instruction2 (1): 59–89. doi:10.1207/s1532690xci0201_3.8.^Cooper, G., & Sweller, J. (1987). "Effects of schema acquisitionand rule automation on mathematical problem-solving transfer". Journal of Educational Psychology79 (4): 347–362.doi:10.1037/0022-0663.79.4.347.9.^ a b Mayer, R.E. (1997). "Multimedia Learning: Are We Asking theRight Questions?". Educational Psychologist32 (41): 1–19.doi:10.1207/s1*******ep3201_1.10.^ Mayer, R.E. (2001). Multimedia Learning. Cambridge: CambridgeUniversity Press. ISBN0-521-78239-2.11.^Mayer, R.E., Bove, W. Bryman, A. Mars, R. & Tapangco, L. (1996)."When Less Is More: Meaningful Learning From Visual and Verbal Summaries of Science Textbook Lessons". Journal of Educational Psychology88 (1): 64–73. doi:10.1037/0022-0663.88.1.64.12.^ Mayer, R.E., Steinhoff, K., Bower, G. and Mars, R. (1995). "Agenerative theory of textbook design: Using annotated illustrations to foster meaningful learning of science text". Educational TechnologyResearch and Development43 (1): 31–41. doi:10.1007/BF02300480.13.^Paas, F., Renkl, A. & Sweller, J. (2004). "Cognitive Load Theory:Instructional Implications of the Interaction between InformationStructures and Cognitive Architecture". Instructional Science32: 1–8.doi:10.1023/B:TRUC.0000021806.17516.d0.14.^ Clark, R.C., Mayer, R.E. (2002). e-Learning and the Science ofInstruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. San Francisco: Pfeiffer. ISBN0-7879-6051-9.15.^ Clark, R.C., Nguyen, F., and Sweller, J. (2006). Efficiency inLearning: Evidence-Based Guidelines to Manage Cognitive Load. SanFrancisco: Pfeiffer. ISBN0-7879-7728-4.16.^Conole G., and Fill K., “A learning design toolkit to createpedagogically effective learning activities”. Journal of Interactive Media in Education, 2005 (08).17.^Carr-Chellman A. and Duchastel P., “The ideal online course,”British Journal of Educational Technology, 31(3), 229-241, July 2000.18.^Koper R., “Current Research in Learning Design,” EducationalTechnology & Society, 9 (1), 13-22, 2006.19.^Britain S., “A Review of Learning Design: Concept,Specifications and Tools” A report for the JISC E-learning Pedagogy Programme, May 2004.20.^IMS Learning Design webpage21.^ a b Piskurich, G.M. (2006). Rapid Instructional Design: LearningID fast and right.22.^ Saettler, P. (1990). The evolution of American educationaltechnology.23.^ Stolovitch, H.D., & Keeps, E. (1999). Handbook of humanperformance technology.24.^ Kelley, T., & Littman, J. (2005). The ten faces of innovation:IDEO's strategies for beating the devil's advocate & driving creativity throughout your organization. New York: Doubleday.25.^ Hokanson, B., & Miller, C. (2009). Role-based design: Acontemporary framework for innovation and creativity in instructional design. Educational Technology, 49(2), 21–28.26.^ a b c Dick, Walter, Lou Carey, and James O. Carey (2005) [1978].The Systematic Design of Instruction(6th ed.). Allyn & Bacon. pp. 1–12.ISBN020*******./?id=sYQCAAAACAAJ&dq=the+systematic+design+of+instruction.27.^ Esseff, Peter J. and Esseff, Mary Sullivan (1998) [1970].Instructional Development Learning System (IDLS) (8th ed.). ESF Press.pp. 1–12. ISBN1582830371. /Materials.html.28.^/Materials.htmlExternal links∙Instructional Design - An overview of Instructional Design∙ISD Handbook∙Edutech wiki: Instructional design model [1]∙Debby Kalk, Real World Instructional Design InterviewRetrieved from "/wiki/Instructional_design" Categories: Educational technology | Educational psychology | Learning | Pedagogy | Communication design | Curricula。
rdmulti 1.1 软件包用户指南：分析 RD 设计中的多个阈值或评分说明书
Package‘rdmulti’June20,2023Type PackageTitle Analysis of RD Designs with Multiple Cutoffs or ScoresVersion1.1Author Matias D.Cattaneo,Rocio Titiunik,Gonzalo Vazquez-BareMaintainer Gonzalo Vazquez-Bare<******************.edu>Description The regression discontinuity(RD)design is a popular quasi-experimental de-sign for causal inference and policy evaluation.The'rdmulti'package provides tools to ana-lyze RD designs with multiple cutoffs or scores:rdmc()estimates pooled and cutoff specific ef-fects for multi-cutoff designs,rdmcplot()draws RD plots for multi-cutoff designs and rdms()es-timates effects in cumulative cutoffs or multi-score designs.See Cattaneo,Titiunik and Vazquez-Bare(2020)<https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2020_Stata.pdf>for further methodological details. Imports ggplot2,rdrobustLicense GPL-2Encoding UTF-8RoxygenNote7.2.3NeedsCompilation noRepository CRANDate/Publication2023-06-2021:00:02UTCR topics documented:rdmulti-package (2)rdmc (3)rdmcplot (6)rdms (8)Index1212rdmulti-package rdmulti-package rdmulti:analysis of RD Designs with multiple cutoffs or scoresDescriptionThe regression discontinuity(RD)design is a popular quasi-experimental design for causal infer-ence and policy evaluation.The rdmulti package provides tools to analyze RD designs with multiple cutoffs or scores:rdmc()estimates pooled and cutoff-specific effects in multi-cutoff de-signs,rdmcplot()draws RD plots for multi-cutoff RD designs and rdms()estimates effects in cumulative cutoffs or multi-score designs.For more details,and related Stata and R packages useful for analysis of RD designs,visit https://rdpackages.github.io/.Author(s)Matias Cattaneo,Princeton University.<**********************>Rocio Titiunik,Princeton University.<**********************>Gonzalo Vazquez-Bare,UC Santa Barbara.<******************.edu>ReferencesCalonico,S.,M.D.Cattaneo,M.Farrell and R.Titiunik.(2017).rdrobust:Software for Regres-sion Discontinuity Designs.Stata Journal17(2):372-404.Calonico,S.,M.D.Cattaneo,and R.Titiunik.(2014).Robust Data-Driven Inference in the Regression-Discontinuity Design.Stata Journal14(4):909-946.Calonico,S.,M.D.Cattaneo,and R.Titiunik.(2015).rdrobust:An R Package for Robust Non-parametric Inference in Regression-Discontinuity Designs.R Journal7(1):38-51.Cattaneo,M.D.,L.Keele,R.Titiunik and G.Vazquez-Bare.(2016).Interpreting Regression Dis-continuity Designs with Multiple Cutoffs.Journal of Politics78(4):1229-1248.Cattaneo,M.D.,L.Keele,R.Titiunik and G.Vazquez-Bare.(2020).Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs.Journal of the American Statistical As-sociation116(536):1941,1952.Cattaneo,M.D.,R.Titiunik and G.Vazquez-Bare.(2020).Analysis of Regression Discontinuity Designs with Multiple Cutoffs or Multiple Scores.Stata Journal20(4):866-891.Keele,L.and R.Titiunik.(2015).Geographic Boundaries as Regression Discontinuities.Political Analysis23(1):127-155rdmc3 rdmc Analysis of RD designs with multiple cutoffsDescriptionrdmc()analyzes RD designs with multiple cutoffs.Usagerdmc(Y,X,C,fuzzy=NULL,derivvec=NULL,pooled_opt=NULL,verbose=FALSE,pvec=NULL,qvec=NULL,hmat=NULL,bmat=NULL,rhovec=NULL,covs_mat=NULL,covs_list=NULL,covs_dropvec=NULL,kernelvec=NULL,weightsvec=NULL,bwselectvec=NULL,scaleparvec=NULL,scaleregulvec=NULL,masspointsvec=NULL,bwcheckvec=NULL,bwrestrictvec=NULL,stdvarsvec=NULL,vcevec=NULL,nnmatchvec=NULL,cluster=NULL,level=95,plot=FALSE,conventional=FALSE)ArgumentsY outcome variable.X running variable.4rdmcC cutoff variable.fuzzy specifies a fuzzy design.See rdrobust()for details.derivvec vector of cutoff-specific order of derivatives.See rdrobust()for details.pooled_opt options to be passed to rdrobust()to calculate pooled estimand.verbose displays the output from rdrobust for estimating the pooled estimand.pvec vector of cutoff-specific polynomial orders.See rdrobust()for details.qvec vector of cutoff-specific polynomial orders for bias estimation.See rdrobust()for details.hmat matrix of cutoff-specific bandwidths.See rdrobust()for details.bmat matrix of cutoff-specific bandwidths for bias estimation.See rdrobust()fordetails.rhovec vector of cutoff-specific values of rho.See rdrobust()for details.covs_mat matrix of covariates.See rdrobust()for details.covs_list list of covariates to be used in each cutoff.covs_dropvec vector indicating whether collinear covariates should be dropped at each cutoff.See rdrobust()for details.kernelvec vector of cutoff-specific kernels.See rdrobust()for details.weightsvec vector of length equal to the number of cutoffs indicating the names of the vari-ables to be used as weights in each cutoff.See rdrobust()for details.bwselectvec vector of cutoff-specific bandwidth selection methods.See rdrobust()for de-tails.scaleparvec vector of cutoff-specific scale parameters.See rdrobust()for details.scaleregulvec vector of cutoff-specific scale regularization parameters.See rdrobust()fordetails.masspointsvec vector indicating how to handle repeated values at each cutoff.See rdrobust()for details.bwcheckvec vector indicating the value of bwcheck at each cutoff.See rdrobust()for de-tails.bwrestrictvec vector indicating whether computed bandwidths are restricted to the range orrunvar at each cutoff.See rdrobust()for details.stdvarsvec vector indicating whether variables are standardized at each cutoff.See rdrobust() for details.vcevec vector of cutoff-specific variance-covariance estimation methods.See rdrobust() for details.nnmatchvec vector of cutoff-specific nearest neighbors for variance estimation.See rdrobust() for details.cluster cluster ID variable.See rdrobust()for details.level confidence level for confidence intervals.See rdrobust()for details.plot plots cutoff-specific estimates and weights.conventional reports conventional,instead of robust-bias corrected,p-values and confidenceintervals.rdmc5Valuetau pooled estimatese.rb robust bias corrected standard error for pooled estimatepv.rb robust bias corrected p-value for pooled estimateci.rb.l left limit of robust bias corrected CI for pooled estimateci.rb.r right limit of robust bias corrected CI for pooled estimatehl bandwidth to the left of the cutoff for pooled estimatehr bandwidth to the right of the cutofffor pooled estimateNhl sample size within bandwidth to the left of the cutoff for pooled estimateNhr sample size within bandwidth to the right of the cutoff for pooled estimateB vector of bias-corrected estimatesV vector of robust variances of the estimatesCoefs vector of conventional estimatesW vector of weights for each cutoff-specific estimateNh vector of sample sizes within bandwidthCI robust bias-corrected confidence intervalsH matrix of bandwidthsPv vector of robust p-valuesrdrobust.resultsresults from rdrobust for pooled estimatecfail Cutoffs where rdrobust()encountered problemsAuthor(s)Matias Cattaneo,Princeton University.<**********************>Rocio Titiunik,Princeton University.<**********************>Gonzalo Vazquez-Bare,UC Santa Barbara.<******************.edu>ReferencesCattaneo,M.D.,R.Titiunik and G.Vazquez-Bare.(2020).Analysis of Regression Discontinuity Designs with Multiple Cutoffs or Multiple Scores.Stata Journal,forthcoming.Examples#Toy datasetX<-runif(1000,0,100)C<-c(rep(33,500),rep(66,500))Y<-(1+X+(X>=C))*(C==33)+(.5+.5*X+.8*(X>=C))*(C==66)+rnorm(1000)#rdmc with standard syntaxtmp<-rdmc(Y,X,C)rdmcplot RD plots with multiple cutoffs.Descriptionrdmcplot()RD plots with multiple cutoffs.Usagerdmcplot(Y,X,C,nbinsmat=NULL,binselectvec=NULL,scalevec=NULL,supportmat=NULL,pvec=NULL,hmat=NULL,kernelvec=NULL,weightsvec=NULL,covs_mat=NULL,covs_list=NULL,covs_evalvec=NULL,covs_dropvec=NULL,ci=NULL,col_bins=NULL,pch_bins=NULL,col_poly=NULL,lty_poly=NULL,col_xline=NULL,lty_xline=NULL,nobins=FALSE,nopoly=FALSE,noxline=FALSE,nodraw=FALSE)ArgumentsY outcome variable.X running variable.C cutoff variable.nbinsmat matrix of cutoff-specific number of bins.See rdplot()for details.binselectvec vector of cutoff-specific bins selection method.See rdplot()for details.scalevec vector of cutoff-specific scale factors.See rdplot()for details.supportmat matrix of cutoff-specific support conditions.See rdplot()for details..pvec vector of cutoff-specific polynomial orders.See rdplot()for details.hmat matrix of cutoff-specific bandwidths.See rdplot()for details.kernelvec vector of cutoff-specific kernels.See rdplot()for details.weightsvec vector of cutoff-specific weights.See rdplot()for details.covs_mat matrix of covariates.See rdplot()for details.covs_list list of of covariates to be used in each cutoff.covs_evalvec vector indicating the evaluation point for additional covariates.See rdrobust() for details.covs_dropvec vector indicating whether collinear covariates should be dropped at each cutoff.See rdrobust()for details.ci adds confidence intervals of the specified level to the plot.See rdrobust()for details.col_bins vector of colors for bins.pch_bins vector of characters(pch)type for bins.col_poly vector of colors for polynomial curves.lty_poly vector of lty for polynomial curves.col_xline vector of colors for vertical lines.lty_xline vector of lty for vertical lines.nobins omits bins plot.nopoly omits polynomial curve plot.noxline omits vertical lines indicating the cutoffs.nodraw omits plot.Valueclist list of cutoffscnum number of cutoffsX0matrix of X values for control unitsX1matrix of X values for treated unitsYhat0estimated polynomial for control unitsYhat1estimated polynomial for treated unitsXmean bin average of X valuesYmean bin average for Y valuesCI_l lower end of confidence intervalsCI_r upper end of confidence intervalscfail Cutoffs where rdrobust()encountered problems8rdmsAuthor(s)Matias Cattaneo,Princeton University.<**********************>Rocio Titiunik,Princeton University.<**********************>Gonzalo Vazquez-Bare,UC Santa Barbara.<******************.edu>ReferencesCattaneo,M.D.,R.Titiunik and G.Vazquez-Bare.(2020).Analysis of Regression Discontinuity Designs with Multiple Cutoffs or Multiple Scores.Stata Journal,forthcoming.Examples#Toy datasetX<-runif(1000,0,100)C<-c(rep(33,500),rep(66,500))Y<-(1+X+(X>=C))*(C==33)+(.5+.5*X+.8*(X>=C))*(C==66)+rnorm(1000)#rdmcplot with standard syntaxtmp<-rdmcplot(Y,X,C)rdms Analysis of RD designs with cumulative cutoffs or two running vari-ablesDescriptionrdms()analyzes RD designs with cumulative cutoffs or two running variables.Usagerdms(Y,X,C,X2=NULL,zvar=NULL,C2=NULL,rangemat=NULL,xnorm=NULL,fuzzy=NULL,derivvec=NULL,pooled_opt=NULL,pvec=NULL,qvec=NULL,hmat=NULL,bmat=NULL,rdms9 rhovec=NULL,covs_mat=NULL,covs_list=NULL,covs_dropvec=NULL,kernelvec=NULL,weightsvec=NULL,bwselectvec=NULL,scaleparvec=NULL,scaleregulvec=NULL,masspointsvec=NULL,bwcheckvec=NULL,bwrestrictvec=NULL,stdvarsvec=NULL,vcevec=NULL,nnmatchvec=NULL,cluster=NULL,level=95,plot=FALSE,conventional=FALSE)ArgumentsY outcome variable.X running variable.C vector of cutoffs.X2if specified,second running variable.zvar if X2is specified,treatment indicator.C2if specified,second vector of cutoffs.rangemat matrix of cutoff-specific ranges for the running variable.xnorm normalized running variable to estimate pooled effect.fuzzy specifies a fuzzy design.See rdrobust()for details.derivvec vector of cutoff-specific order of derivatives.See rdrobust()for details.pooled_opt options to be passed to rdrobust()to calculate pooled estimand.pvec vector of cutoff-specific polynomial orders.See rdrobust()for details.qvec vector of cutoff-specific polynomial orders for bias estimation.See rdrobust() for details.hmat matrix of cutoff-specific bandwidths.See rdrobust()for details.bmat matrix of cutoff-specific bandwidths for bias estimation.See rdrobust()for details.rhovec vector of cutoff-specific values of rho.See rdrobust()for details.covs_mat matrix of covariates.See rdplot()for details.covs_list list of of covariates to be used in each cutoff.10rdms covs_dropvec vector indicating whether collinear covariates should be dropped at each cutoff.See rdrobust()for details.kernelvec vector of cutoff-specific kernels.See rdrobust()for details.weightsvec vector of length equal to the number of cutoffs indicating the names of the vari-ables to be used as weights in each cutoff.See rdrobust()for details.bwselectvec vector of cutoff-specific bandwidth selection methods.See rdrobust()for de-tails.scaleparvec vector of cutoff-specific scale parameters.See rdrobust()for details.scaleregulvec vector of cutoff-specific scale regularization parameters.See rdrobust()fordetails.masspointsvec vector indicating how to handle repeated values at each cutoff.See rdrobust()for details.bwcheckvec vector indicating the value of bwcheck at each cutoff.See rdrobust()for de-tails.bwrestrictvec vector indicating whether computed bandwidths are restricted to the range orrunvar at each cutoff.See rdrobust()for details.stdvarsvec vector indicating whether variables are standardized at each cutoff.See rdrobust() for details.vcevec vector of cutoff-specific variance-covariance estimation methods.See rdrobust() for details.nnmatchvec vector of cutoff-specific nearest neighbors for variance estimation.See rdrobust() for details.cluster cluster ID variable.See rdrobust()for details.level confidence level for confidence intervals.See rdrobust()for details.plot plots cutoff-specific and pooled estimates.conventional reports conventional,instead of robust-bias corrected,p-values and confidenceintervals.ValueB vector of bias-corrected coefficientsV variance-covariance matrix of the estimatorsCoefs vector of conventional coefficientsNh vector of sample sizes within bandwidth at each cutoffCI bias corrected confidence intervalsH bandwidth used at each cutoffPv vector of robust p-valuesAuthor(s)Matias Cattaneo,Princeton University.<**********************>Rocio Titiunik,Princeton University.<**********************>Gonzalo Vazquez-Bare,UC Santa Barbara.<******************.edu>rdms11ReferencesCattaneo,M.D.,R.Titiunik and G.Vazquez-Bare.(2020).Analysis of Regression Discontinuity Designs with Multiple Cutoffs or Multiple Scores.Stata Journal,forthcoming.Examples#Toy dataset:cumulative cutoffsX<-runif(1000,0,100)C<-c(33,66)Y<-(1+X)*(X<C[1])+(0.8+0.8*X)*(X>=C[1]&X<C[2])+(1.2+1.2*X)*(X>=C[2])+rnorm(1000) #rmds:basic syntaxtmp<-rdms(Y,X,C)Index_PACKAGE(rdmulti-package),2rdmc,2,3rdmcplot,2,6rdms,2,8rdmulti-package,2rdmulti_package(rdmulti-package),212。