R是一种用于统计计算和图形的编程语言，由R核心团队和R统计计算基金会支持。R由统计学家Ross Ihaka和Robert Gentleman创建，在数据挖掘者和统计学家中被用于数据分析和开发统计软件。用户已经创建了软件包来增强R语言的功能。

assignmentutor-lab™ 为您的留学生涯保驾护航 在代写R语言方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写R语言代写方面经验极为丰富，各种代写R语言相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

CS代写|R语言代写R language代考|Time series

Longitudinal data consist of repeated measurements, usually done over time, on the same experimental units. Longitudinal data, when replicated on several experimental units at each time point, are called repeated measurements, while when not replicated, they are called time series. Base R provides special support for the analysis of time series data, while repeated measurements can be analyzed with nested linear models, mixed-effects models, and additive models.

Time series data are data collected in such a way that there is only one observation, possibly of multiple variables, available at each point in time. This brief section introduces only the most basic aspects of time-series analysis. In most cases time steps are of uniform duration and occur regularly, which simplifies data handling and storage. R not only provides methods for the analysis and manipulation of time-series, but also a specialized class for their storage, “ts”. Regular time steps allow more compact storage-e.g., a ts object does not need to store time values for each observation but instead a combination of two of start time, step size and end time.

We start by creating a time series from a numeric vector. By now, you surely guessed that you need to use a constructor called ts() or a conversion constructor called as.ts () and that you can look up the arguments they accept by reading the corresponding help pages.
For example for a time series of monthly values we could use:
my.ts <- ts $(1: 10$, start $=2019$, deltat $=1 / 12)$
class (my.ts)

$\operatorname{str}$ (my.ts)

We next use the data set austres with data on the number of Australian residents and included in R.

CS代写|R语言代写R language代考|Sharing of R-language extensions

The most elegant way of adding new features or capabilities to $R$ is through packages. This is without doubt the best mechanism when these extensions to $R$ need to be shared. However, in most situations it is also the best mechanism for managing code that will be reused even by a single person over time. $R$ packages have strict rules about their contents, file structure, and documentation, which makes it possible among other things for the package documentation to be merged into R’s help system when a package is loaded. With a few exceptions, packages can be written so that they will work on any computer where $R$ runs.

Packages can be shared as source or binary package files, sent for example through e-mail. However, for sharing packages widely, it is best to submit them to a repository. The largest public repository of R packages is called CRAN, an acronym for Comprehensive R Archive Network. Packages available through CRAN are guaranteed to work, in the sense of not failing any tests built into the package and not crashing or aborting prematurely. They are tested daily, as they may depend on other packages whose code will change when updated. In January 2017, the number of packages available through CRAN passed the 10,000 mark.

A key repository for bioinformatics with $\mathrm{R}$ is Bioconductor, containing packages that pass strict quality tests. Recently, ROpenScience has established guidelines and a system for code peer review for packages. These peer-reviewed packages are available through CRAN or other repositories and listed at the ROpenScience website. In some cases you may need or want to install less stable code from Git repositories such as versions still under development not yet submitted to CRAN. Using the package ‘devtools’ we can install packages directly from GitHub, Bitbucket and other code repositories based on Git. Installations from code repositories are always installations from sources (see below). It is of course also possible to install packages from local files (e.g., after a manual download).

One good way of learning how the extensions provided by a package work, is by experimenting with them. When using a function we are not yet familiar with, looking at its help to check all its features will expand your “toolbox.” How much documentation is included with packages varies, while documentation of exported objects is enforced, many packages include, in addition, comprehensive user guides or articles as vignettes. It is not unusual to decide which package to use from a set of alternatives based on the quality of available documentation. In the case of packages adding extensive new functionality, they may be documented in depth in a book. Well-known examples are Mixed-Effects Models in $S$ and S-Plus (Pinheiro and Bates 2000), Lattice: Multivariate Data Visualization with $R$ (Sarkar 2008) and ggplot2: Elegant Graphics for Data Analysis (Wickham and Sievert 2016).

R语言代考

CS代写|R语言代写R language代考|Time series

my.ts <- ts(1:10， 开始=2019, 出席=1/12)

有限元方法代写

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MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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