开发历程.md:

GITHUB

hulinhui-code/hulinhui: Hu Linhui's Personal Package

端导入，但要注意把函数名以export(function_name)写入NAMESPACE文件，否则将会显示没有这个函数名。
6. 为避免新安装包占用C盘空间，可在R包的写一个自定义安装程序包函数，将包一律安装至为D盘路径。
7. 更新包后

experiments/20180703_按等级分开和同比计算设计.Rmd:

GITHUB

ahorawzy/Mreport: Monthly Report of traffic condition

也用不到，所以可以用非加权平均数模拟2017年5月数据，由此开发环比计算框架。
### 尝试
```{r}

aws: AWS downloader

GITHUB

RajLabMSSM/downloadR: echoverse module: Single- and multi-threaded downloading functions

R: AWS downloader
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML

mrpt_calc_mpv: 计算中后台费用

GITHUB

takewiki/jlrdspkg: package for jlrds app

R: 计算中后台费用
mrpt_calc_mpvR Documentation
计算中后台费用

aws: AWS for local constant models on a grid

GITHUB

WIAS-BERLIN/aws: Adaptive Weights Smoothing

R: AWS for local constant models on a grid
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

aws: AWS for local constant models on a grid

GITHUB

neuroconductor-releases/aws: Adaptive Weights Smoothing

R: AWS for local constant models on a grid
awsR Documentation
AWS for local constant models on a grid

aws: AWS for local constant models on a grid

CRAN

aws: Adaptive Weights Smoothing

R: AWS for local constant models on a grid
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

aws: AWS for local constant models on a grid

GITHUB

neuroconductor/aws: Adaptive Weights Smoothing

R: AWS for local constant models on a grid
awsR Documentation
AWS for local constant models on a grid

awes: awes - Adaptively Weighted Ensembles via Stacking

GITHUB

reichlab/adaptively-weighted-ensemble: Adaptively Weighted Ensembles via Stacking

R: awes - Adaptively Weighted Ensembles via Stacking
awesR Documentation
awes - Adaptively Weighted Ensembles via

f_weibo_app_followtags: 文本向量词云

GITHUB

yibochen/weiBor: fetch and analyse weibo data

R: 文本向量词云
f_weibo_app_followtagsR Documentation
文本向量词云

task/20182920_货运计算1.Rmd:

GITHUB

ahorawzy/Mreport: Monthly Report of traffic condition

x2$湖北省 <- NULL
```{r}
write.csv(x2,file = "D:\\交大云同步\\实习\\19_货运分析\\计算结果\\三市月平均日总行驶量.csv")

aws-class: Class '"aws"'

GITHUB

neuroconductor/aws: Adaptive Weights Smoothing

R: Class '"aws"'
aws-classR Documentation
Class "aws"

aws: Adaptive Weights Smoothing

CRAN

aws: Adaptive Weights Smoothing

Package: aws
Version: 2.5-6
Date: 2024-09-29

inst/doc/aws-Example.pdf
inst/doc/aws-Example.R

aws-class: Class '"aws"'

GITHUB

neuroconductor-releases/aws: Adaptive Weights Smoothing

R: Class '"aws"'
aws-classR Documentation
Class "aws"

aws-class: Class '"aws"'

CRAN

aws: Adaptive Weights Smoothing

R: Class '"aws"'
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML() {

aws-class: Class '"aws"'

GITHUB

WIAS-BERLIN/aws: Adaptive Weights Smoothing

R: Class '"aws"'
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML() {

mrpt_calc_cumSum_Period: 计算累计数

GITHUB

takewiki/jlrdspkg: package for jlrds app

R: 计算累计数
mrpt_calc_cumSum_PeriodR Documentation
计算累计数

mrpt_calc_cumSum_item: 计算累计数

GITHUB

takewiki/jlrdspkg: package for jlrds app

R: 计算累计数
mrpt_calc_cumSum_itemR Documentation
计算累计数

leeper/r-aws: A basic wrapper to the AWS Java

GITHUB

leeper/r-aws: A basic wrapper to the AWS Java

Package: r-AWS
Type: Package
Title: A basic wrapper to the AWS Java

inst/aws-java-sdk/currentVersion.txt
inst/aws-java-sdk/lib/aws-java-sdk-1.1.0-javadoc.jar

mrpt_graph_rollup2_step1: 计算品牌全渠道的费用合计

GITHUB

takewiki/jlrdspkg: package for jlrds app

R: 计算品牌全渠道的费用合计
mrpt_graph_rollup2_step1R Documentation
计算品牌全渠道的费用合计