This document provides an overview of the EViews command language and programming. It describes the four main components of the EViews command language: commands, functions, object views and procs, and object data members. It provides examples of common commands like wfopen, series, and equation. It also discusses functions, object views and procs, data members, and basic programming concepts in EViews like variables, control structures like if/else and for loops, and program arguments.
This presentation is a part of the COP2272C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce students to the C++ language and the fundamentals of object orientated programming..
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
This document provides instructions for using EViews to replicate the regression analysis examples from the textbook "Using Econometrics: A Practical Guide". It describes how to create an EViews workfile, enter data, generate variables, create groups, and graph relationships. Specifically, it demonstrates analyzing data on height and weight from the textbook by entering the data, creating a group containing the height and weight series, and graphing their relationship to visually confirm the hypothesized positive relationship.
MuleSoft Nashik Virtual Meetup#3 - Deep Dive Into DataWeave and its ModuleJitendra Bafna
Deep Dive Into DataWeave and its Modules
The document discusses DataWeave, MuleSoft's data transformation language. It covers DataWeave modules, operators, working with arrays and objects, and Mule runtime features. Key topics include DataWeave fundamentals like data types, reading/writing data, variables, operators, and flow control. Functions, filtering, mapping, reducing, and updating arrays and objects are also summarized.
Scala is a multi-paradigm programming language that blends object-oriented and functional programming. It is designed to express common programming patterns in a concise, elegant, and type-safe way. Scala runs on the Java Virtual Machine and interoperates seamlessly with Java, but also integrates concepts from languages such as Haskell, ML and Ruby. Some key features of Scala include support for functional programming, a static type system with type inference, pattern matching, actors and immutable data structures.
Some key features of Scala include:
1. It allows blending of functional programming and object-oriented programming for more concise and powerful code.
2. The static type system allows for type safety while maintaining expressiveness through type inference, implicits, and other features.
3. Scala code interoperates seamlessly with existing Java code and libraries due to its compatibility with the JVM.
Perl, a cross-platform, open-source computer programming language used widely in the commercial and private computing sectors. Perl is a favourite among Web developers for its flexible, continually evolving text-processing and problem-solving capabilities.
EViews is a statistical software package that allows users to analyze time series econometric data. It utilizes a graphical user interface and works with objects like series, groups, and equations stored in workfiles. Workfiles contain objects like data, estimations, and views. Common object types are series for single columns of data, groups for multiple columns of data, and equations for estimation results. Users can open, view, and manipulate these objects to conduct analyses.
This presentation is a part of the COP2272C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce students to the C++ language and the fundamentals of object orientated programming..
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
This document provides instructions for using EViews to replicate the regression analysis examples from the textbook "Using Econometrics: A Practical Guide". It describes how to create an EViews workfile, enter data, generate variables, create groups, and graph relationships. Specifically, it demonstrates analyzing data on height and weight from the textbook by entering the data, creating a group containing the height and weight series, and graphing their relationship to visually confirm the hypothesized positive relationship.
MuleSoft Nashik Virtual Meetup#3 - Deep Dive Into DataWeave and its ModuleJitendra Bafna
Deep Dive Into DataWeave and its Modules
The document discusses DataWeave, MuleSoft's data transformation language. It covers DataWeave modules, operators, working with arrays and objects, and Mule runtime features. Key topics include DataWeave fundamentals like data types, reading/writing data, variables, operators, and flow control. Functions, filtering, mapping, reducing, and updating arrays and objects are also summarized.
Scala is a multi-paradigm programming language that blends object-oriented and functional programming. It is designed to express common programming patterns in a concise, elegant, and type-safe way. Scala runs on the Java Virtual Machine and interoperates seamlessly with Java, but also integrates concepts from languages such as Haskell, ML and Ruby. Some key features of Scala include support for functional programming, a static type system with type inference, pattern matching, actors and immutable data structures.
Some key features of Scala include:
1. It allows blending of functional programming and object-oriented programming for more concise and powerful code.
2. The static type system allows for type safety while maintaining expressiveness through type inference, implicits, and other features.
3. Scala code interoperates seamlessly with existing Java code and libraries due to its compatibility with the JVM.
Perl, a cross-platform, open-source computer programming language used widely in the commercial and private computing sectors. Perl is a favourite among Web developers for its flexible, continually evolving text-processing and problem-solving capabilities.
EViews is a statistical software package that allows users to analyze time series econometric data. It utilizes a graphical user interface and works with objects like series, groups, and equations stored in workfiles. Workfiles contain objects like data, estimations, and views. Common object types are series for single columns of data, groups for multiple columns of data, and equations for estimation results. Users can open, view, and manipulate these objects to conduct analyses.
In java , I want you to implement a Data Structure known as a Doubly.pdfaromalcom
In java , I want you to implement a Data Structure known as a Doubly-Ended-Queue. it is a
“fair” data structure in that it implements a FIFO (First In First Out ) behavior. As such, it is
often used to implement various wait lists in computer systems. For example, jobs waiting to use
the CPU, jobs waiting for a printer, jobs waiting to be placed into RAM for execution. In short,
whenever we want a fair strategy for waiting we use queues.
A DEQUE (Doubly-ended-queue) is a related data structure. Although similar to a Queue, it
differs in that it allows for insertions AND deletions from either end of the list (both the front
and the rear).
Your implementation MUST use a doubly-linked-list implementation. You may not use a static
array implementation.
Thus, a Deque is a List but it is one which only concerns itself with the first and last positions for
any insertion or deletion. The 6 operations supported are :
public void insertFront( int item ) - insert the given item (as a node) into the first position of the
Deque.
public void insertRear( int item ) - insert the given item (as a node) into the last position of the
Deque.
public int deleteFront( ) - delete and return the element stored in the first node of the Deque.
public int deletRear( ) – delete and return the element stored in the last node of the Deque.
public boolean isempty( ) - returns true if the Deque is currently empty or false if it is not.
public void printDeque( ) - print the integers from the list, one per line, from the first element
through to the last in order.
Classes
Your program must implement the following 3 classes. public class dequeDriver
This class will contain your program’s main method. It will need to declare a deque object and
process input as indicated below.
Your program should prompt the user for the path of an input file. It should open the file for
input and process it line by line. Each line of the input file will have one of the following forms.
PR
IF
IR
DF
DR
The meanings of each input is as follows:
PR - print the current contents of the deque from front to rear using the printDeque( ) method of
the deque object.
IF - insert the given int value into the front of the deque.
IR - insert the given int value into the rear of the deque.
DF - delete the front value from the deque.
DR – delete the rear element of the deque.
Below is an example input file that your program should be able to process.
PR
IF 4
IF 5
IF 6
IR 7
PR
DR
PR
DF
PR
The output for the input file shown above is :
EMPTY DEQUE
----- Front -----
6
5
4
7
----- Rear -----
----- Front -----
6
5
4
----- Rear -----
----- Front -----
5
4
----- Rear -----
public class dequeNode
This class will implement the linked nodes that will be used to implement the deque itself.
It should have the following protected data members.
protected dequeNode next; // next pointer to next node
protected dequeNode prev; // previous pointer to previous node
protected int val; // the integer value stored within the dequeNod.
The document provides an introduction and overview of Scala concepts. In 3 sentences:
Scala is an object-oriented and functional language that runs on the Java Virtual Machine. It combines object-oriented and functional programming which allows for modularity, extensibility, and composition. The document discusses Scala concepts like expressions, types, values, classes, traits, objects, pattern matching and more to build a fundamental understanding of how Scala programs work.
Martin Odersky outlines the growth and adoption of Scala over the past 6 years and discusses Scala's future direction over the next 5 years. Key points include:
- Scala has grown from its first classroom use in 2003 to filling a full day of talks at JavaOne in 2009 and developing a large user community.
- Scala 2.8 will include new collections, package objects, named/default parameters, and improved tool support.
- Over the next 5 years, Scala will focus on concurrency and parallelism features at all levels from primitives to tools.
- Other areas of focus include extended libraries, performance improvements, and standardized compiler plugin architecture.
Scala 2.8 will include improvements to collections, package objects, named and default parameters, and faster generics through specialization. Over the next 5 years, focus will be on concurrency and parallelism at all levels. Better tools like an improved REPL and IDE integration are also planned. The essence of Scala is concentrating on powerful abstractions to reduce boilerplate and allow powerful libraries.
This document summarizes Martin Odersky's talk about the past, present, and future of the Scala programming language over the next 5 years. It discusses Scala's growth in popularity and adoption from 2003-2009. It also outlines new features in the 2.8 version including improved collections, package objects, named parameters, and improved tooling. Finally, it discusses focus areas for the next 5 years including improved concurrency, parallelism, static typing, and language abstractions to support reliable concurrent programming.
Martin Odersky discusses the past, present, and future of Scala over the past 5 years and next 5 years. Key points include:
- Scala has grown significantly in usage and community over the past 6 years since its first release.
- Scala 2.8 will include improvements like new collections, package objects, named/default parameters, and better tool support.
- Over the next 5 years, Scala will focus on improving concurrency and parallelism through better abstractions, analyses, and static typing support.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, create visualizations and build predictive models. Key features of R include its extensive library of statistical and machine learning methods, ability to handle large datasets, and functionality for data wrangling, modeling, visualization and communication of results. The document provides instructions on downloading and installing R and RStudio, loading and installing packages, and introduces basic R programming concepts like vectors, matrices, data frames and factors.
MATLAB stands for Matrix Laboratory. MATLAB was written originally
to provide easy access to matrix software developed by the LINPACK (linear system package) and matlab 2012a manual pdf
R can be used to analyze data and perform statistical analysis. Key functions include help() and ? to get information on functions, and objects() to view stored objects. Vectors can be created with c() and manipulated using arithmetic operators. Matrices are two-dimensional arrays that can be operated on using *, /, and t(). Larger datasets are typically read from external files using read.table() or read.delim(). Common distributions can be explored using functions like dnorm(), pnorm(), and rnorm(). Statistical analysis includes commands like cov() and cor() to measure covariance and correlation between variables.
React Native allows developers to build mobile apps using React with native platform capabilities. It uses native components instead of web views, making apps feel and perform like native ones. The document discusses what React Native is, how to set up a development environment, build a basic app, add libraries, handle common errors, and React Native fundamentals like components, styles, layout, events, and touch handling.
This document summarizes and compares several programming languages - C#, Visual Basic, F#, C++/CLI, and the common intermediate language (CIL) - by providing concise code examples and explanations of each language.
This document provides an introduction to using R for statistical analysis and visualization. It discusses what R is, why it is useful, and 12 reasons to learn R. These include benefits like rigor in data analysis, reproducibility through scripting, access to cutting-edge statistical methods, powerful and customizable graphics, and that it is free and open-source. The document then provides resources for learning R, including tutorials, packages of interest, and how to download the software. It concludes with exercises walking through basic R concepts like vectors, matrices, data frames, importing data from a CSV file, subsetting data, and simple plotting.
R can be used to analyze data and perform statistical analysis. Functions like help(), ? and help.start() provide information about other functions. Objects created in R sessions are stored by name and can be removed with rm(). Vectors like x=c(1,2,3,4,5) can be created and their length checked with length(x). Subsets of vectors can be selected using logical or integer indexes inside square brackets. Matrices are multi-dimensional generalizations of vectors that can be manipulated using operators like * and %. Data can be read into R from external files using functions like read.table() and read.delim(). Common statistical distributions like normal, uniform and exponential are available as functions in R for
The document describes the project for an advanced compiler construction course. Students will build an optimizing compiler for a simple language called PL241. The compiler will first parse the language and generate an intermediate representation with control flow and dependency information. It will then implement optimizations like common subexpression elimination and register allocation. Finally, students will generate optimized code for a target platform like x86 or DLX. The project involves multiple steps to showcase different compiler techniques.
The Ring programming language version 1.5.2 book - Part 21 of 181Mahmoud Samir Fayed
This document provides information about functions and recursion in the Ring programming language. It discusses defining functions, calling functions, declaring parameters, sending parameters, the main function, variable scope, return values, and recursion. Specifically, it explains that Ring supports recursion through functions calling themselves with different parameters, and provides an example of a recursive factorial function.
The document is a slide presentation on family and human sexuality that covers topics such as: global views of the family including composition and types; kinship and authority patterns; theoretical perspectives on family including functionalist, conflict, and feminist; marriage and mate selection processes; variations in family forms like single parents and stepfamilies; alternatives like divorce; and diverse lifestyles including cohabitation and remaining single. It provides definitions and comparisons of different family structures around the world.
This document discusses illegal immigration policy, outlining causes of illegal migration like poverty, education, and trade liberalization. It notes problems illegal immigrants face such as lack of access to services, slavery, death, and kidnapping. The economic impact is explored, including consumer demand, budgetary impacts, and wage impacts. Undocumented immigrants contribute to the economy and jobs while some studies find a small adverse wage impact for lower-skilled native workers.
In java , I want you to implement a Data Structure known as a Doubly.pdfaromalcom
In java , I want you to implement a Data Structure known as a Doubly-Ended-Queue. it is a
“fair” data structure in that it implements a FIFO (First In First Out ) behavior. As such, it is
often used to implement various wait lists in computer systems. For example, jobs waiting to use
the CPU, jobs waiting for a printer, jobs waiting to be placed into RAM for execution. In short,
whenever we want a fair strategy for waiting we use queues.
A DEQUE (Doubly-ended-queue) is a related data structure. Although similar to a Queue, it
differs in that it allows for insertions AND deletions from either end of the list (both the front
and the rear).
Your implementation MUST use a doubly-linked-list implementation. You may not use a static
array implementation.
Thus, a Deque is a List but it is one which only concerns itself with the first and last positions for
any insertion or deletion. The 6 operations supported are :
public void insertFront( int item ) - insert the given item (as a node) into the first position of the
Deque.
public void insertRear( int item ) - insert the given item (as a node) into the last position of the
Deque.
public int deleteFront( ) - delete and return the element stored in the first node of the Deque.
public int deletRear( ) – delete and return the element stored in the last node of the Deque.
public boolean isempty( ) - returns true if the Deque is currently empty or false if it is not.
public void printDeque( ) - print the integers from the list, one per line, from the first element
through to the last in order.
Classes
Your program must implement the following 3 classes. public class dequeDriver
This class will contain your program’s main method. It will need to declare a deque object and
process input as indicated below.
Your program should prompt the user for the path of an input file. It should open the file for
input and process it line by line. Each line of the input file will have one of the following forms.
PR
IF
IR
DF
DR
The meanings of each input is as follows:
PR - print the current contents of the deque from front to rear using the printDeque( ) method of
the deque object.
IF - insert the given int value into the front of the deque.
IR - insert the given int value into the rear of the deque.
DF - delete the front value from the deque.
DR – delete the rear element of the deque.
Below is an example input file that your program should be able to process.
PR
IF 4
IF 5
IF 6
IR 7
PR
DR
PR
DF
PR
The output for the input file shown above is :
EMPTY DEQUE
----- Front -----
6
5
4
7
----- Rear -----
----- Front -----
6
5
4
----- Rear -----
----- Front -----
5
4
----- Rear -----
public class dequeNode
This class will implement the linked nodes that will be used to implement the deque itself.
It should have the following protected data members.
protected dequeNode next; // next pointer to next node
protected dequeNode prev; // previous pointer to previous node
protected int val; // the integer value stored within the dequeNod.
The document provides an introduction and overview of Scala concepts. In 3 sentences:
Scala is an object-oriented and functional language that runs on the Java Virtual Machine. It combines object-oriented and functional programming which allows for modularity, extensibility, and composition. The document discusses Scala concepts like expressions, types, values, classes, traits, objects, pattern matching and more to build a fundamental understanding of how Scala programs work.
Martin Odersky outlines the growth and adoption of Scala over the past 6 years and discusses Scala's future direction over the next 5 years. Key points include:
- Scala has grown from its first classroom use in 2003 to filling a full day of talks at JavaOne in 2009 and developing a large user community.
- Scala 2.8 will include new collections, package objects, named/default parameters, and improved tool support.
- Over the next 5 years, Scala will focus on concurrency and parallelism features at all levels from primitives to tools.
- Other areas of focus include extended libraries, performance improvements, and standardized compiler plugin architecture.
Scala 2.8 will include improvements to collections, package objects, named and default parameters, and faster generics through specialization. Over the next 5 years, focus will be on concurrency and parallelism at all levels. Better tools like an improved REPL and IDE integration are also planned. The essence of Scala is concentrating on powerful abstractions to reduce boilerplate and allow powerful libraries.
This document summarizes Martin Odersky's talk about the past, present, and future of the Scala programming language over the next 5 years. It discusses Scala's growth in popularity and adoption from 2003-2009. It also outlines new features in the 2.8 version including improved collections, package objects, named parameters, and improved tooling. Finally, it discusses focus areas for the next 5 years including improved concurrency, parallelism, static typing, and language abstractions to support reliable concurrent programming.
Martin Odersky discusses the past, present, and future of Scala over the past 5 years and next 5 years. Key points include:
- Scala has grown significantly in usage and community over the past 6 years since its first release.
- Scala 2.8 will include improvements like new collections, package objects, named/default parameters, and better tool support.
- Over the next 5 years, Scala will focus on improving concurrency and parallelism through better abstractions, analyses, and static typing support.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, create visualizations and build predictive models. Key features of R include its extensive library of statistical and machine learning methods, ability to handle large datasets, and functionality for data wrangling, modeling, visualization and communication of results. The document provides instructions on downloading and installing R and RStudio, loading and installing packages, and introduces basic R programming concepts like vectors, matrices, data frames and factors.
MATLAB stands for Matrix Laboratory. MATLAB was written originally
to provide easy access to matrix software developed by the LINPACK (linear system package) and matlab 2012a manual pdf
R can be used to analyze data and perform statistical analysis. Key functions include help() and ? to get information on functions, and objects() to view stored objects. Vectors can be created with c() and manipulated using arithmetic operators. Matrices are two-dimensional arrays that can be operated on using *, /, and t(). Larger datasets are typically read from external files using read.table() or read.delim(). Common distributions can be explored using functions like dnorm(), pnorm(), and rnorm(). Statistical analysis includes commands like cov() and cor() to measure covariance and correlation between variables.
React Native allows developers to build mobile apps using React with native platform capabilities. It uses native components instead of web views, making apps feel and perform like native ones. The document discusses what React Native is, how to set up a development environment, build a basic app, add libraries, handle common errors, and React Native fundamentals like components, styles, layout, events, and touch handling.
This document summarizes and compares several programming languages - C#, Visual Basic, F#, C++/CLI, and the common intermediate language (CIL) - by providing concise code examples and explanations of each language.
This document provides an introduction to using R for statistical analysis and visualization. It discusses what R is, why it is useful, and 12 reasons to learn R. These include benefits like rigor in data analysis, reproducibility through scripting, access to cutting-edge statistical methods, powerful and customizable graphics, and that it is free and open-source. The document then provides resources for learning R, including tutorials, packages of interest, and how to download the software. It concludes with exercises walking through basic R concepts like vectors, matrices, data frames, importing data from a CSV file, subsetting data, and simple plotting.
R can be used to analyze data and perform statistical analysis. Functions like help(), ? and help.start() provide information about other functions. Objects created in R sessions are stored by name and can be removed with rm(). Vectors like x=c(1,2,3,4,5) can be created and their length checked with length(x). Subsets of vectors can be selected using logical or integer indexes inside square brackets. Matrices are multi-dimensional generalizations of vectors that can be manipulated using operators like * and %. Data can be read into R from external files using functions like read.table() and read.delim(). Common statistical distributions like normal, uniform and exponential are available as functions in R for
The document describes the project for an advanced compiler construction course. Students will build an optimizing compiler for a simple language called PL241. The compiler will first parse the language and generate an intermediate representation with control flow and dependency information. It will then implement optimizations like common subexpression elimination and register allocation. Finally, students will generate optimized code for a target platform like x86 or DLX. The project involves multiple steps to showcase different compiler techniques.
The Ring programming language version 1.5.2 book - Part 21 of 181Mahmoud Samir Fayed
This document provides information about functions and recursion in the Ring programming language. It discusses defining functions, calling functions, declaring parameters, sending parameters, the main function, variable scope, return values, and recursion. Specifically, it explains that Ring supports recursion through functions calling themselves with different parameters, and provides an example of a recursive factorial function.
The document is a slide presentation on family and human sexuality that covers topics such as: global views of the family including composition and types; kinship and authority patterns; theoretical perspectives on family including functionalist, conflict, and feminist; marriage and mate selection processes; variations in family forms like single parents and stepfamilies; alternatives like divorce; and diverse lifestyles including cohabitation and remaining single. It provides definitions and comparisons of different family structures around the world.
This document discusses illegal immigration policy, outlining causes of illegal migration like poverty, education, and trade liberalization. It notes problems illegal immigrants face such as lack of access to services, slavery, death, and kidnapping. The economic impact is explored, including consumer demand, budgetary impacts, and wage impacts. Undocumented immigrants contribute to the economy and jobs while some studies find a small adverse wage impact for lower-skilled native workers.
This document provides an introduction to key concepts in statistics including:
1) Data consists of observations and measurements while statistics involves collecting, organizing, analyzing and interpreting data to make decisions.
2) A population is the total collection of interest while a sample is a subset, parameters describe populations and statistics describe samples.
3) Descriptive statistics involves summarizing and displaying data while inferential statistics uses samples to draw conclusions about populations.
New Microsoft PowerPoint Presentation.pptxMohamedSahal16
1) Social movements emerge due to feelings of relative deprivation and the mobilization of resources. Leaders help channel discontent and mobilize groups through conventional or unconventional means.
2) Social change occurs slowly over time through cultural changes or more rapidly through conflict and resistance from groups with vested interests. Technological advances have brought dramatic social changes globally.
3) Increased globalization and computer technology have connected more people while also raising issues of unequal access. Migration of laborers has economic benefits but also creates social tensions, as host countries and migrants both face challenges in adjusting.
Logistic regression is used to model dichotomous outcome variables based on one or more predictor variables. It models the probability of an outcome as a function of the predictors using a logistic link function. The model estimates regression coefficients that represent the log odds ratios of the predictors. Odds ratios above 1 indicate increased odds of the outcome with higher predictor values, while odds ratios below 1 indicate decreased odds. Logistic regression allows testing of individual predictor coefficients and the overall model fit. Loglinear models generalize logistic regression to situations with categorical predictors where there is no clear outcome variable.
The document discusses planning reports and proposals. It covers adapting the three-step writing process to reports and proposals, different types of reports including informational and analytical reports, organizing information reports, and three ways to organize analytical reports including focusing on conclusions, recommendations, and logical arguments. It also discusses planning proposals and different types of proposals as well as organizational strategies for proposals.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
3. The EViews Command Language
The EViews command language may be split into four groups:
• Commands
• Functions
• Object Views and Procs
• Object Data Members
3
4. EViews Commands
EViews commands tell EViews to:
• Manipulate object containers (Workfiles, Pages, Databases).
• Create new objects.
• Show, copy, delete, rename or close objects.
• Set Program options.
A list of all EViews commands can be found in Chapter 12 of the
EViews 9 Command and Programming Reference (PDF available
from the EViews Help Menu).
4
5. EViews Commands: Container Manipulation
The most common type of EViews commands are those that
create, open, or interact with EViews workfiles.
In general these commands start with "wf":
• wfopen – open an existing workfile on disk, or a foreign file.
• wfcreate – create a new workfile.
• wfclose – close a workfile.
• wfsave – save the current workfile as an EViews file or a foreign
format.
• wfselect – change the current active workfile.
• wfrefresh – refresh the links in the current workfile.
5
6. EViews Commands: Container Manipulation
Commands that manipulate pages generally start with "page":
• pageload – load a new page in the current workfile.
• pagecreate – create a new page in the current workfile.
• pagedelete – delete a page in the current workfile.
• pageselect – change the current active workfile page.
• pagestruct – restructure the current page.
• pagerename – rename a page in the current workfile.
6
7. EViews Commands: Container Manipulation
Similarly, commands that work on databases start with "db".
Other object container commands include:
• smpl – change the sample of the current workfile page.
• import – import data into the current workfile page.
• fetch – fetch data from a database into the current workfile
page.
• store – store data from the current workfile page into a
database.
7
8. EViews Commands: Object Creation
Object creation commands (often called object declaration
commands) are used to create new objects in the current workfile.
The commands are, generally, simply the type of object, followed
by the name of the new object:
• series x – create a new series called "X".
• equation eq1 – create a new equation called "EQ1".
• group g – create a new group called "G".
8
9. EViews Commands: Object Creation
Often you can follow the command with further specification for the
object:
• series x = @nrnd – create a new series called "X" filled with
normal random numbers.
• group g x y z – create a new group called "G" containing the
series "X", "Y" and "Z".
The full syntax for each object's declaration command can be
found at the start of each object's section of Chapter 1. of the
EViews 8 Object Reference.
9
10. EViews Commands: Object
show/copy/rename/delete/close
The following commands work on workfile objects
• show – display a view of an object.
• freeze – freeze the view of an object into a new object.
• copy – copy the object from workfile to workfile, or page to page,
or create a copy in the current page.
• rename – rename an object.
• delete – delete an object.
• close – close an object (if it object is currently being shown).
10
11. EViews Functions
EViews functions are used to assign values to Series or Alpha
objects, or Matrix and Scalar objects, or program variables.
Functions generally start with an "@" symbol, and are preceded by
an object name (and, possibly declaration) and an "=". For
example:
series x = @nrnd
Here the @nrnd function is used to assign standard normal random
numbers to the series X.
A list of all EViews functions can be found in Chapter 14 of the
EViews 9 Command and Programming Reference. Full details of
the functions can be found in Chapter 13.
11
12. EViews Functions
There are many types of EViews functions:
12
• Basic mathematical
• Time series
• Financial
• Descriptive Statistics
• Cumulative Statistics
• Moving Statistics
• Group Row
• By-Group
• Trigonometric
• Statistical Distribution
• String
• Date
• Indicator
• Workfile & Information
13. EViews Functions
Some examples:
13
series x = @log(y)
series x = @pch(y)
series x = @pv(r,n,y)
scalar x = @mean(y)
series x = @cumsum(y)
series x = @movav(y,3)
series x = @rsum(g)
series x = @minsby(y,s)
!x = @tan(y)
scalar x = @cnorm(y)
!x = @instr(%y, "he")
string x = @strnow
series x = @trend
Create a series, X, and assign the values of log(Y) to it.
Assign the one-period percentage change in Y to X.
Present value of Y, given a rate of R and N periods.
Create a scalar, X, equal to the mean of Y.
Cumulative sum of Y.
Three period moving average of Y.
Row-sum of the group, G.
Minimum values of Y for each category of S.
Assign the tangent of the program variable !y to !x.
Cumulative normal distribution at value Y.
Find the position of the phrase "he" in the string %y.
Create a string object containing the current date/time.
Create a trend series.
14. Object Views and Procs
The EViews workfile is a collection of objects. Each object type has
different Views and Procs available to it, and they are generally
accessed by clicking on either the View menu or the Proc menu
when an object is open.
Each object view and proc has a command line equivalent. The
general syntax of object views and procs is:
object.view(options) arguments
i.e. the name of the workfile object, followed by a dot, then the
view/proc name, followed by options and any arguments.
The Views and Procs available to each object type are listed in the
EViews 9 Object Reference.
14
15. Object Views and Procs
Some examples:
15
show eq01.stats
show gdp.line
freeze(gr1) gdp.line
eq01.forecast yf
unemp.smooth unemps
show gp.coint(s)
Show the regression output of the equation object EQ01.
Show a line graph of the series GDP.
Freeze the graph of GDP into a new graph object, GR1.
Forecast EQ01, storing the forecast values into series YF.
Exponential smoothing on UNEMP, and save to UNEMPS.
Show the summary cointegration test results for group GP.
16. Object Data Members
Along with the Views and Procs available to each object type,
objects also contain "data members". These are retrievable
objects that contain information about the parent object.
Data members are only available via command. There is no mouse
equivalent. The syntax for retrieving a data member is always:
=object.@member
Data members always return strings, scalars, or matrix objects.
Note, they never return series or alpha objects.
16
17. Object Data Members
Data members are stored with the object. EViews, in general, does
not need to perform calculations to retrieve the members (unlike
Views and Procs).
Each object's data members are listed at the start of the object's
section of the EViews 9 Object Reference.
17
18. Object Data Members
Some examples:
18
=unemp.@displayname
=gdp.@first
scalar x = eq01.@r2
matrix x = eq01.@coefcov
vector x = eq01.@tstats
=g1.@count
=g1.@seriesname(1)
Returns the UNEMP series' display name.
Returns the date of the first non-NA in GDP.
Stores the R-squared from EQ01 into the scalar X.
Saves EQ01's coefficient covariance matrix into matrix X.
Saves the t-statistics into vector X.
Returns the number of series in the group G1.
Returns the name of the first series in the group G1.
20. Creating a New Program
EViews programs are merely a collection EViews commands
collected together in a text file named with a .prg extension. As
such any text editor can be used to create an EViews program.
20
However the easiest
way to create one is to
use EViews itself, by
clicking on
File->New->Program.
21. Loading and Saving Programs
To open an existing program, simply click on File->Open->Program.
To save an open program, click on File->Save.
21
22. Running a Program
Once you have created your program, you can run it by clicking on
the Run button.
22
This brings up the Run dialog,
which contains some program
execution options. We’ll ignore
those for now, and just hit the OK
button.
23. A Simple Program
The simplest program is just a collection of simple commands:
23
24. Program Comments
EViews uses the apostrophe as a comment character in programs.
Any text following a ' and on the same line will be ignored by the
program.
Example:
series x = @nrnd 'this is a comment
Only the "series x = @nrnd" part of this line is executed by EViews.
24
26. Program Variables
Program variables are variables that only exist when the program is
running. Once the program has finished, the variables disappear.
They do not form part of your workfile.
There are two types of program variables. Numeric and string
variables. Numeric variables start with an “!”. String variables start
with a “%”.
Tip: Due to their temporary nature, displaying program variables is
difficult. For debugging purposes it is often useful to temporarily
assign program variables to workfile objects (such as scalars,
strings or tables) to help you follow what is happening to your
program variables.
26
27. ! Variables
! variables are numeric scalar program variables. They can be used
in most mathematical calculations in a program.
Examples:
!x = 3
!y = 3+2
!z = !x + !y
!pi = 4*@atan(1)
series y = @sqrt(!z)
27
28. % Variables
% variables are character/string program variables. They contain
quoted text.
You can concatenate two strings with a + sign. You may also use
string functions to assign to a % variable.
Examples:
%x = "hello"
%y = "my name is" + %name
%pi = "3.142"
%z = "The date/time is " + @strnow
28
29. Replacement Variables
You can use % variables in two ways. The first is as an string actual
string – i.e. anywhere EViews expects a string value, you can use a
% variable instead.
The second way is as a "replacement". Replacement variables are
used to substitute the variable with its string value.
To instruct EViews to use a % variable as a replacement, rather
than as a string, enclose the % variable inside braces {}.
29
30. Replacement Variables
Example:
%x = "gdp"
series y = %x
series z = {%x}
The first line assigns "gdp" to the % variable %x.
The second line is read by EViews as:
series y = "gdp"
This will error (series are numeric and cannot contain text).
The third line, however, is read by EViews as:
series z = gdp
30
31. Replacement Variables
A good rule of thumb to follow when deciding whether you should
enclose your % variable inside braces or not, is to think "Does
EViews expect quotes in this command?" If EViews is expecting
quotes, leave the % variable as it is. If it is not expecting quotes,
uses the braces.
series y = {%z} + {%z}
scalar p = @instr(%z, %y)
%z = "name"
alpha a = %z
alpha b = {%z}
Note the difference between the last two – the first sets A equal to
"name", and the second sets B equal to the alpha series called
NAME.
31
32. Program Arguments
Program arguments are special % string variables whose value may
be changed every time you run the program. The individual
arguments are called "%0", "%1", "%2" and so on.
You can set the arguments using the Run dialog:
32
Simply enter a space delimited
list of values in the Program
arguments box.
33. Program Arguments
For example, take the following program line:
equation eq1.ls {%0} C {%1}
If you enter "Y X" into the Program arguments box on the run dialog,
then the program will execute the line:
equation eq1.ls Y C X
Whereas entering "GDP UNEMP" would execute:
equation eq1.ls GDP C UNEMP
33
35. If/else/endif statements
If statements are used when you wish to execute a line of code only
if a certain condition is met. The basic syntax is:
if [condition] then
'line of code to execute
endif
[condition] must be an expression that evaluates to a scalar value of
1 or 0, or a scalar true/false. Note this means you cannot, in
general, use a series expression as part of an if statement.
35
36. If/else/endif statements
Examples:
if 1+1=2 then
wfcreate a 1990 2000
endif
Since 1+1 does equal 2, the wfcreate command will be executed
(creating an annual workfile between 1990 and 2000).
if !p>3 then
equation eq1.ls y c x1 x2
endif
Only if the program variable, !p, is greater than three will equation
EQ1 be estimated.
36
37. If/else/endif statements
if @instr(%x, "gdp")>0 then
var v1.ls 1 3 {%x} unemp m2
endif
Only if the program variable %x contains the string "gdp" will the
VAR be estimated.
if @max(sales)>1000000 then
show price.hist
endif
If the maximum value of SALES is greater than 1,000,000, show the
histogram and descriptive statistics of the series PRICE.
37
38. If/else/endif statements
You may use an else statement to tell EViews what to do if the
condition is not met. The basic syntax is:
if [condition] then
'line of code to execute if true
else
'line of code to execute if not true
endif
38
39. If/else/endif statements
Example:
if !p>3 then
equation eq1.ls y c x1 x2 x3
else
equation eq1.ls y c z1 z2 z3
endif
If !p is greater than 3, equation Y is regressed against X1, X2 and
X3. Otherwise Y is regressed against Z1, Z2 and Z3.
39
40. If statements and series/samples
As already mentioned, you may not use an if statement on a series
expression. If you want to conditionally assign values to a series you
must either use a sample, or the @recode function.
Examples:
smpl if x<0
series y = 100
smpl if x<=0
series y = 200
smpl @all
series z = @recode(x<0, 100, 200) 40
41. For Loops
EViews supports two types of for loop; numerical and string.
Numerical for loops take the form:
for !i=1 to 10
'lines to be repeatedly executed
next
A scalar variable (either a ! variable, as shown here, or a scalar
object in the workfile) is used to control the loop. Each time through
the loop the scalar variable is incremented by 1. The loop stops
once the variable reaches its terminal value (here 10).
41
42. For Loops
Examples:
for !i=1 to 5
series x{!i} = @nrnd
next
This loop generates 5 random normal series, X1, X2, X3, X4 and
X5.
for !i=1 to 10
equation eq1.ls y c x{!i}
next
Five equations are created, each regressing Y against a single X
variable.
42
43. For Loops
You may optionally add a step statement to change how much the
control variable increases at each iteration of the loop:
for !i=1 to 10 step 2
series x{!i} = @nrnd
next
This loop generates 5 random normal series, X1, X3, X5, X7 and
X9.
for !i=20 to 1 step -5
equation eq1.ls y c x{!i}
next
Four equations are created, each regressing Y against a single X
variable, first X20, then X15, then X10, then X5 43
44. For Loops
String for loops simply loop a string control variable over different
string values.
String for loops take the form:
for %j [space delimited list of values]
'lines to be repeatedly executed
next
A string variable (either a % variable, as shown here, or a string
object in the workfile) is used to control the loop. Each time through
the loop the control variable is set equal to the next value in the
given list.
44
45. For Loops
Examples:
for %j gdp unemp time
series {%j} = @nrnd
next
This loop generates 3 random normal series, GDP, UNEMP and
TIME.
%regs = "sales_uk sales_usa sales_fra"
for %k {%regs}
equation eq1.ls {%k} c demand_global
next
Three equations are created, each with a different dependent
variable, but the same independent variable. 45
47. Maximum Number of Errors
By default, an EViews program will stop as soon as EViews issues
an error. There are two methods available to override this behavior:
• Use the Run dialog to increase the "Maximum errors before
halting"
• Use the setmaxerrs command within the program to dynamically
change the number of errors allowed. 47
48. Maximum Number of Errors
Example:
wfcreate m 1990 2000
series y=@nrnd
series x=@nrnd
series w=@nrnd
series z=3
for %reg x z w
equation eq_{%reg}.ls y c {%reg}
next
This program loops through the series X, Z and W, performing a
regression of Y against a constant and each of those series, one at
a time. It will cause an error when it regresses against Z, since Z is
perfectly collinear with the constant.
48
49. Maximum Number of Errors
Increasing the maximum number of errors to something greater than
1 in the Run dialog will let the program continue past the error.
Similarly, adding a setmaxerrs line to the program will allow it to
run:
wfcreate m 1990 2000
series y=@nrnd
series x=@nrnd
series w=@nrnd
series z=3
setmaxerrs 100
for %reg x z w
equation eq_{%reg}.ls y c {%reg}
next 49
51. Running/Executing Programs
There are a number of ways to instruct EViews to run a program:
• The Run button/dialog.
• The Run command.
• The Exec command.
• The Include command.
51
52. Run Command
The Run command can be issued from the EViews command line
to instruct EViews to open and run a program. The syntax is:
run myprogram.prg [arguments]
Simply give the name of the program (with a path, if required)
following the Run command.
You may add arguments as a space delimited list following the
name of the program.
52
54. Run Command
You may also use the Run command inside a program to launch
and run a second program.
For example:
54
55. Run Command
However, the Run command has one drawback when being used
inside a program: EViews will not return to the calling program
once the child program has finished running. For example:
In this program the line estimating equation eq2 will never execute.
EViews stops all program execution once run_monthly.prg has
finished.
55
56. Exec Command
The Exec command works in the same was as the Run command,
with one main differences; unlike the Run command, EViews will
continuing executing the calling program after an Exec.
This program will execute the run_monthly.prg program, and then
continue to execute the following lines, creating the equation eq2.
56
57. Exec Command
Note that with the Exec command (and Run) each program's
program variables are isolated. i.e. setting a variable, such as !i, in
the parent program will not interfere with a variable with the same
name in the child program.
Similarly, any subroutines defined in the child program will not be
available in the calling program.
57
58. Include
The final way to execute a program from within a program is with
the include command.
Include is similar to Exec with a couple of differences:
• Unlike Exec, program variables are not isolated – the child
program can interfere with program variables used in the parent
• Subroutines are available to the parent program.
58
59. Tips
• Run should never be used in a program
• Include should only be used when loading a "library" of
subroutines – the included program should not actually execute
any code, merely provide subroutines for use in the parent
program.
• Exec should be used for all other times you wish to run a child
program.
59
61. Subroutine Definition
EViews subroutines are user-defined functions that exist within the
scope of the EViews programming language.
Subroutines allow you to define a set of commands in one part of
your program and then call (or execute) those commands
repeatedly in other parts of your program.
The basic syntax to define the start of a subroutine is to use the
subroutine keyword, followed by the name of the subroutine.
The subroutine ends with the endsub keyword.
61
63. Calling Subroutines
To execute the code inside a subroutine, the call keyword is
used, followed by the name of the subroutine you wish to execute.
63
64. Subroutine Arguments
Subroutines may include arguments, allowing you to define the
variables used within the subroutine.
To declare arguments with your subroutine you define the type of
object being passed in, as well as its name:
subroutine name(obj_type1 arg1, obj_type2 arg2, …)
The name of the arguments in your subroutine do not (and should
not) be the name of objects in your workfile.
64
66. Subroutine and Argument Placement
Subroutines can be placed anywhere in your program: at the start,
at the end, or spread throughout the program. They need not be
defined above the call statement that calls them.
Subroutine arguments can be inputs (i.e. objects that the
subroutine uses) or outputs (i.e. objects that the subroutine
produces), or both. There is no way to indicate that an object is an
input or an output.
66
67. Tips
• Subroutine should all be placed at the start of the program file for
easy debugging access.
• If you have many subroutines, define them in a separate program
file and then include that file at the top of your program.
• Define your subroutines such that outputs come first in the
argument list (or come last). Don't mix inputs and outputs.
• For even greater clarity, name the arguments with a name that
indicates whether the argument is an input or output.
67
68. Subroutine Strings and Scalars
When defining a subroutine, you will often want to use a string or a
scalar value as one of the arguments. You can choose whether to
use a string/scalar workfile object, or a program variable as the
argument:
subroutine foo(string a, scalar b)
subroutine goo(string %a, scalar !b)
Here foo uses workfile objects, and goo uses program variables
(note the use of % and ! in the argument names).
68
69. Subroutine Strings and Scalars
When calling a subroutine with a string or scalar argument you can
use a program variable, a workfile object, or a value, whether you
defined the subroutine with or without a program variable.
subroutine foo(scalar b)
subroutine goo(scalar !b)
call foo(myscalar)
call foo(!x)
call foo(3)
call goo(myscalar)
call goo(!x)
call goo(3)
69
70. Subroutine Strings and Scalars
Thus there is very little difference between defining your
subroutines with either objects or program variables when using
strings or scalars.
The one exception is if the subroutine changes workfile page.
When using a workfile object as an argument, changing the
workfile page means that the object is no longer available inside
the subroutine.
70
71. Subroutine Strings and Scalars
For example the following program will error inside foo because the
scalar a is not available on the new page, so x cannot be created.
Using goo instead will work just fine.
71
73. User Dialogs
EViews provides a number of functions for the creation of custom
dialogs inside an EViews program.
These custom dialogs (or User Dialogs to use the EViews term)
allow the program to present information, or collect information,
from the person running the program.
They provide a more elegant way of providing program input
options than program arguments, program options, or simply letting
the user configure some settings at the top of the program.
73
74. User Dialogs
The simplest form of User Dialog is the @uiprompt("msg")
function.
It simply produces a message box for the user containing the
message msg:
74
75. User Dialogs
Other simple User Dialogs functions:
The iostring or ioscalar arguments are both an input and output.
They decide what is pre-filled in in the dialog, and they return the
user's input to the dialog.
75
Function Description Full Syntax
@uiedit Produces an edit field. @uiedit(string iostring, string prompt)
@uilist Produces a list box.
Has two versions, one
returns a string, the
other a scalar.
@uilist(string iostring, string prompt, string list)
@uilist(scalar ioscalar, string prompt, string list)
@uiradio Produces a set of radio
buttons.
@uiradio(scalar ioscalar, string prompt, string list)
76. User Dialogs
All User Dialogs are EViews functions, and as such have return
values. The value returned depends upon whether the user clicked
on the "OK" button or the "Cancel" button.
If a user clicks "OK" on the User Dialog, the function will return a
value of 0. "Cancel" returns -1.
76
77. @uidialog
The final User Dialog function is @uidialog, and it is the most
complicated, allowing you to build extensive dialogs with many
different controls inside them.
To specify a dialog with @uidialog, you must enter a list of
control types and the arguments for each control type.
77
78. @uidialog
The controls are:
@uidialog also returns a 0 or -1 depending on whether "OK" or
"Cancel" was pressed, unless there is a custom button control, in
which case pressing the button rather than "OK" or "Cancel" will
return a positive integer. 78
Control Arguments Description
"edit" string iostring, string prompt Edit box
"list" string iostring, string prompt, string list
scalar ioscalar, string prompt, string list
List box
"radio" scalar ioscalar, string prompt, string list Set of radio buttons
"check" scalar ioscalar, string prompt Single check box
"caption" string caption Dialog title
"button" string buttontext Custom button
80. EViews Add-ins
EViews Add-ins are ways of extending the features available in
EViews via the menu system, or via the command language.
A number of Add-ins are available to download by clicking on the
Add-ins menu.
However, the most important thing about Add-ins (at least for this
class) is that Add-ins are really nothing more than EViews
programs.
Any program you write can be turned into an EViews Add-in, simply
by using the Manage Add-ins menu item.
80
81. Adding Add-ins
When you add an Add-in to your copy of EViews, you may specify
whether the Add-in is available via the menu system, via a
command, or both.
You may also choose whether to make your Add-in Global or
Object-specific.
Global add-ins are available at all times, and always appear in the
Add-ins menu. Object-specific add-ins only appear in the Add-ins
menu when an object of that type is open.
81
82. addin
Rather than using the menu system to register/add an Add-in, you
may add one via the addin command. The syntax is:
addin(type=type, docs=docspath, menu=menutext,
proc=proctext) programname
(There are other options also available – see the Command
Reference entry for more details).
Type should be the object type of the Add-in, or "Global" for a
Global Add-in.
Docspath should be the full path and file name of any
documentation accompanying the Add-in.
Menutext should be the text to add to the Add-ins menu (if any).
Proctext should be the command line given to the Add-in.
82
83. _this
Object-specific Add-ins work on whatever object is currently open.
An equation-specific Add-in might, for example, use the coefficient
covariance matrix to perform a post-estimation diagnostic.
The problem is that you probably won't know the name of the
currently open object, so referencing the data-members, or
accessing views and procs of the currently open object could be
tricky.
83
84. _this
Thankfully, EViews provides the _this keyword. _this simply tells
EViews that you want to work with the current open object.
All of the object data members, procs and views are available by
using _this rather than the object's name.
Thus:
_this.hist will show a histogram of the current open object
(if the current open object is a series).
_this.@coefcov will access the equation's coefficient
covariance matrix (if the current open object is an equation).
84