- Syllabi App at The Wharton School
- The R Statistical Computing Environment: The Basics and Beyond (Berkeley, CA)
- Statistical Computing with R and Stata
- About this book
Syllabi App at The Wharton School
Research support Current research candidates A guide to research at Charles Sturt University Professional development Writing programs Data, methods and tools Data analysis software Statistics workshops and tools Outputs and reporting Indigenous support Policies and procedures Support contacts Forms and guides Bulletin. Funding and grants Find a funding opportunity Applying for and managing a grant Developing an application Application approval and submission Budget development and resources Establish and manage funded projects Contract development and negotiation ARC and NHMRC annual rounds.
Supervision Register Responsibilities Admission of candidates Managing candidates Professional development Resources, forms and guides. R: Statistical Computing.
About R Among R's strengths are its built-in tools for inferential statistics, its compact modelling syntax and its data visualisation capabilities. Training workshop The R introduction workshop runs for 3 hours. Structure of the workshop 1. Cost This is a fee-based workshop to ensure people who register will commit their time to participate.
The R Statistical Computing Environment: The Basics and Beyond (Berkeley, CA)
We have workshops coming up in Albury, Bathurst and Wagga. Free cRow sessions cRow combined R-user-group of Wagga sessions are free and run one a month in Wagga. It includes an effective data handling and storage facility, a suite of operators for calculations on arrays, in particular matrices, a large, coherent, integrated collection of intermediate tools for data analysis, graphical facilities for data analysis and display either on-screen or on hardcopy, and a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
View projects. With the recent progress in the theoretical field of sparse learning problems, current R packages are lagging behind the cutting edge research. Both the UI and Animint2 is a re-write of Animint which is an R package for making interactive animated data visualization on the web using ggplot syntax and two new Automatic differentiation AD is a set of techniques to calculate derivatives automatically.
It generally outperforms non-AD methods like symbolic With the fast adoption of Electronic Health Records EHR in modern healthcare systems, various machine learning methods are developed to conduct Thiloshon Nagarajah bdclean: User friendly biodiversity data cleaning pipeline.
Until recently, biodiversity data was scattered in different formats in natural history collections, survey reports, and in literature. In the last Ashwin Agrawal Biodiversity Data Utilities.
Statistical Computing with R and Stata
The aim of the project is to improve the current functionality of existing data management and cleaning packages for Biodiversity in R and integrate Andrew Connell changepoint. There are many R packages available for offline changepoint detection but, to the knowledge of myself and the mentors, only one for online change Povilas Gibas Darwinazing biodiversity data in R. It includes a glossary of terms in other contexts these might be Wenjing Wang Diagnostic statistics and visualization for quantile regression.
This project aims to extend diagnostic statistics in the R package quokar. CRC Press, This is work in progress. Proper documentation as well as splitting the code into multiple scripts is planned. Skip to content.
About this book
Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up.
R Branch: master New pull request. Find File. Download ZIP. Sign in Sign up.
- Important Links.
- R project for statistical computing - - Google Summer of Code Archive!
- The syndetic paradigm : the untrodden path beyond Freud and Jung.
Launching GitHub Desktop