R is a free open-source implementation of the S statistical computing language and programming environment. The current status of R is a command line driven interface with no advanced cross-platform graphical user interface (GUI), but it includes tools for building such. Over the past years, ...
Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical...
- An Introduction to The Statistical Computing Powerhouse Learn everything you need to know about the R programming language and discover why it is the most widely used language in data science. Summer Worsley 18 min blog R vs SQL - Which Should I Learn? Find out everything you need ...
The R Series(共27册),这套丛书还有 《Statistical Inference via Data Science》《Spatio-Temporal Statistics with R》《Advanced R》《Learning Microeconometrics with R》《Deep Learning and Scientific Computing with R torch》等。 我来说两句 短评 ··· 热门 还没人写过短评呢 我要写书评 Statistic...
Unlike languages such as Python and Java, R is not a general-purpose programming language. Instead, it’s considered a domain-specific language (DSL), meaning its functions and use are designed for a specific area of use or domain. In R’s case, that’s statistical computing and analysis...
The MSS Core consists of six mandatory 3-credit courses covering models, methods, theory, computing, practice, and a 1-credit pro-seminar. If a student possesses substantial prior coursework in one or more of these areas, they may request approval from their advisor and the Master's Director...
How to use the help system and find help from other sources How to get additional libraries of commands R is more than just a program that does statistics. It is a sophisticated computer language and environment for statistical computing and graphics. R is available from the R-Project for St...
Whether you are working with large data volumes or running multiple permutations of your calculations, statistical computing has become essential for today’s statistician. Popular statistical computing practices include: Statistical programming– From traditional analysis of variance and linear regression to ...
There are also facilities for computing dates and time (eg finding the duration between two dates), complex numbers, integral calculations (computing with integers only and not real numbers), do matrix operations, etc. You can make your own formulae, plot graphs, export images created by the ...
This text presents introductory material on computation and programming, with a focus on statistical applications. From the perspective of a typical "statistical computing" text, this one places more emphasis on code writing and computational technicalities than it does on numerical algorithms and their...