[书籍介绍] Mastering Scientific Computing with R [推广有奖]

0关注
564
粉丝

泰斗

还不是VIP/贵宾

威望: 9 级
论坛币: 2694403 个
通用积分: 18514.9619
学术水平: 2744 点
热心指数: 3467 点
信用等级: 2560 点
经验: 484575 点
帖子: 5414
精华: 52
在线时间: 3589 小时
注册时间: 2007-8-6
最后登录: 2024-4-24

楼主

igs816

发表于 2015-3-4 14:15:53 |只看作者 |坛友微信交流群|倒序 |AI写论文

相似文件

换一批

是否 +2 论坛币

k人参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

Paul Gerrard, "Mastering Scientific Computing with R"
English | ISBN: 1783555254 | 2015 | 432 pages | PDF, EPUB | 4 + 24 MB

Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment
About This Book
Perform publication-quality science using R
Use some of R's most powerful and least known features to solve complex scientific computing problems
Learn how to create visual illustrations of scientific results
Who This Book Is For

If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.
In Detail

With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions.

Beginning with an overview of fundamental R concepts, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing for statistically significant differences between groups and model relationships in data. You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation and learn about an advanced analytical method.

pdf: