0.前言
雖然很早就知道R被微軟收購,也很早知道R在統計分析處理方面很強大,開始一直沒有行動過。。。直到php
直到12月初在微軟技術大會,看到我軟的工程師演示R的使用,我就震驚了,而後最近在網上處處瞭解和爬一些R的資料,看着看着就入迷了,這就是個大寶庫了,之前怎麼沒發現,看來仍是太狹隘了。直到前幾天我看到這個Awesome R文檔,我就靜不下來了,對比了目前本身的工做和之後的方向,很是適合我。因此堅決果斷的把這個文檔漢化了,因此你們一塊兒享受吧。html
說明:本文已經提交到github,地址:https://github.com/asxinyu/awesome-R ,因爲我的知識和能力限制,部分組件特別是機器學習方面比較專業的術語沒法翻譯,若是有懂的朋友能夠留言或者在github直接修改。一塊兒完善。 html5
這裏有不少很是不錯的R包和工具. 該想法來自於awesome-machine-learning.java
這裏是包的導航清單,看起來更方便 https://awesome-r.comgit
經過這些翻譯瞭解這些工具包,之後幹活也就方便多了。不過翻譯這個東西的確要靠耐心,翻譯,編輯花費了至少一週的空餘時間。github
在編輯本文的過程當中,驚喜的發現原來 伯樂在線 也在翻譯Awesome系列的其餘資源:地址在github:web
1.
https://github.com/jobbole/awesome-dotnet-cn
2.https://github.com/jobbole/awesome-java-cn
redis
3.https://github.com/jobbole/awesome-javascript-cn
算法
本文原文地址:http://www.cnblogs.com/asxinyu/p/Awesome_R_Chinese.html
1.集成開發環境
2.語法
3.數據操做
4.圖形顯示
5.HTML部件
6.複用組件研究
7.Web技術和服務
8.並行計算
9.高性能
10.語言API
11.數據庫管理
12.機器學習
- AnomalyDetection - 來自Twitter的AnomalyDetection R包. 官網:https://github.com/twitter/AnomalyDetection
- ahaz - 半參數添加風險迴歸的正則化. 官網:http://cran.r-project.org/web/packages/ahaz/index.html
- arules - 挖掘關聯規則和頻繁項集. 官網:http://cran.r-project.org/web/packages/arules/index.html
- bigrf - 大隨機森林:大型數據集的分類和迴歸森林. 官網:http://cran.r-project.org/web/packages/bigrf/index.html
- bigRR - 廣義迴歸(特殊是在p >> n狀況下). 官網:http://cran.r-project.org/web/packages/bigRR/index.html
- bmrm - 風險最小化方案的正規化方法. 官網:http://cran.r-project.org/web/packages/bmrm/index.html
- Boruta - 全部相關的特徵選擇算法的一個封裝 . 官網:http://cran.r-project.org/web/packages/Boruta/index.html
- BreakoutDetection - Breakout Detection via Robust E-Statistics from Twitter.[暫時不明真相] 官網:https://github.com/twitter/BreakoutDetection
- bst - 梯度增長. 官網:http://cran.r-project.org/web/packages/bst/index.html
- CausalImpact - 利用貝葉斯時間序列結構模型進行因果推斷. 官網:https://github.com/google/CausalImpact
- C50 - C5.0決策樹和基於規則的模型. 官網:http://cran.r-project.org/web/packages/C50/index.html
- caret - 分類和迴歸訓練. 官網:http://cran.r-project.org/web/packages/caret/index.html
- Clever Algorithms For Machine Learning
- CORElearn - 分類、迴歸、特徵評價和排序. 官網:http://cran.r-project.org/web/packages/CORElearn/index.html
- CoxBoost - Cox models by likelihood based boosting for a single survival endpoint or competing risks. 官網:http://cran.r-project.org/web/packages/CoxBoost/index.html
- Cubist - 規則和基於實例的迴歸建模. 官網:http://cran.r-project.org/web/packages/Cubist/index.html
- e1071 - Misc統計函數 (e1071),主要功能有類別分析、傅里葉變換,模糊聚類,支持向量機,最短路徑計算,樸素貝葉斯分類器等等. 官網:http://cran.r-project.org/web/packages/e1071/index.html
- earth - 多元自適應迴歸模型. 官網:http://cran.r-project.org/web/packages/earth/index.html
- elasticnet - 稀疏估計和稀疏主成分分析. 官網:http://cran.r-project.org/web/packages/elasticnet/index.html
- ElemStatLearn - 書籍"The Elements of Statistical Learning, Data Mining, Inference, and Prediction"中的數據集,函數和例子. 官網:http://cran.r-project.org/web/packages/ElemStatLearn/index.html
- evtree - 全局最優樹的進化學習. 官網:http://cran.r-project.org/web/packages/evtree/index.html
- forecast - 使用ARIMA, ETS, STLM, TBATS,和神經網絡進行時間序列預測. 官網:http://cran.r-project.org/web/packages/forecast/index.html
- forecastHybrid - 使用"forecast"包對ARIMA, ETS, STLM, TBATS,和神經網絡模型進行交叉檢驗. 官網:http://cran.r-project.org/web/packages/forecastHybrid/index.html
- FSelector - 一個基於subset-search或特性排名方法的特徵選擇框架. 官網:https://cran.r-project.org/web/packages/FSelector/index.html
- frbs - 使用模糊規則系統處理分類和迴歸的任務. 官網:http://cran.r-project.org/web/packages/frbs/index.html
- GAMBoost - 基於廣義線性和加法模型. 官網:http://cran.r-project.org/web/packages/GAMBoost/index.html
- gamboostLSS - GAMLSS方法的改善. 官網:http://cran.r-project.org/web/packages/gamboostLSS/index.html
- gbm - 改善廣義線性模型. 官網:http://cran.r-project.org/web/packages/gbm/index.html
- glmnet - Lasso 和 elastic-net正規化廣義線性模型. 官網:http://cran.r-project.org/web/packages/glmnet/index.html
- glmpath - L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model. 官網:http://cran.r-project.org/web/packages/glmpath/index.html
- GMMBoost - 廣義混合模型. 官網:http://cran.r-project.org/web/packages/GMMBoost/index.html
- grplasso - Fitting user specified models with Group Lasso penalty. 官網:http://cran.r-project.org/web/packages/grplasso/index.html
- grpreg - Regularization paths for regression models with grouped covariates. 官網:http://cran.r-project.org/web/packages/grpreg/index.html
- h2o - Deeplearning, Random forests, GBM, KMeans, PCA, GLM. 官網:http://cran.r-project.org/web/packages/h2o/index.html
- hda - 異方差判別分析. 官網:http://cran.r-project.org/web/packages/hda/index.html
- ipred - 預測器改進. 官網:http://cran.r-project.org/web/packages/ipred/index.html
- kernlab - kernlab: 基於內核學習的機器實驗室. 官網:http://cran.r-project.org/web/packages/kernlab/index.html
- klaR - 分類和可視化. 官網:http://cran.r-project.org/web/packages/klaR/index.html
- kohonen - 監督和非監督自組織映射. 官網:http://cran.r-project.org/web/packages/kohonen/
- lars - Least Angle Regression, Lasso and Forward Stagewise. 官網:http://cran.r-project.org/web/packages/lars/index.html
- lasso2 - L1 constrained estimation aka ‘lasso’. 官網:http://cran.r-project.org/web/packages/lasso2/index.html
- LiblineaR - 基於C/C++庫的線性預測模型. 官網:http://cran.r-project.org/web/packages/LiblineaR/index.html
- lme4 - Mixed-effects models. 官網:https://github.com/lme4/lme4
- LogicReg - 邏輯迴歸模型. 官網:http://cran.r-project.org/web/packages/LogicReg/index.html
- maptree - 映射、修剪和圖形樹模型. 官網:http://cran.r-project.org/web/packages/maptree/index.html
- mboost - Model-Based Boosting. 官網:http://cran.r-project.org/web/packages/mboost/index.html
- Machine Learning For Hackers
- mvpart - Multivariate partitioning. 官網:http://cran.r-project.org/web/packages/mvpart/index.html
- MXNet - MXNet brings flexible and efficient GPU computing and state-of-art deep learning to R. 官網:https://github.com/dmlc/mxnet/tree/master/R-package
- ncvreg - Regularization paths for SCAD- and MCP-penalized regression models. 官網:http://cran.r-project.org/web/packages/ncvreg/index.html
- nnet - eed-forward Neural Networks and Multinomial Log-Linear Models. 官網:http://cran.r-project.org/web/packages/nnet/index.html
- oblique.tree - Oblique Trees for Classification Data. 官網:http://cran.r-project.org/web/packages/oblique.tree/index.html
- pamr - Pam: 小矩陣預測分析. 官網:http://cran.r-project.org/web/packages/pamr/index.html
- party - A Laboratory for Recursive Partytioning. 官網:http://cran.r-project.org/web/packages/party/index.html
- partykit - A Toolkit for Recursive Partytioning. 官網:http://cran.r-project.org/web/packages/partykit/index.html
- penalized - L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model. 官網:http://cran.r-project.org/web/packages/penalized/index.html
- penalizedLDA - Penalized classification using Fisher's linear discriminant. 官網:http://cran.r-project.org/web/packages/penalizedLDA/index.html
- penalizedSVM - 使用懲罰函數的特徵選擇支持向量機. 官網:http://cran.r-project.org/web/packages/penalizedSVM/index.html
- quantregForest - quantregForest: Quantile Regression Forests. 官網:http://cran.r-project.org/web/packages/quantregForest/index.html
- randomForest - 隨機森林: Breiman and Cutler's random forests for classification and regression. 官網:http://cran.r-project.org/web/packages/randomForest/index.html
- randomForestSRC - randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC). 官網:http://cran.r-project.org/web/packages/randomForestSRC/index.html
- rattle - 圖形界面式的數據挖掘工具. 官網:http://cran.r-project.org/web/packages/rattle/index.html
- rda - Shrunken Centroids Regularized Discriminant Analysis. 官網:http://cran.r-project.org/web/packages/rda/index.html
- rdetools - Relevant Dimension Estimation (RDE) in Feature Spaces. 官網:http://cran.r-project.org/web/packages/rdetools/index.html
- REEMtree - Regression Trees with Random Effects for Longitudinal (Panel) Data. 官網:http://cran.r-project.org/web/packages/REEMtree/index.html
- relaxo - Relaxed Lasso. 官網:http://cran.r-project.org/web/packages/relaxo/index.html
- rgenoud - R version of GENetic Optimization Using Derivatives. 官網:http://cran.r-project.org/web/packages/rgenoud/index.html
- rgp - R基因編程框架. 官網:http://cran.r-project.org/web/packages/rgp/index.html
- Rmalschains - 使用本地文化基因算法進行連續問題優化.[這裏翻譯不許]. Search Chains (MA-LS-Chains) in R. 官網:http://cran.r-project.org/web/packages/Rmalschains/index.html
- rminer - 在分類和迴歸問題中簡單的使用數據挖掘方法(如神經網絡和支持向量機). 官網:http://cran.r-project.org/web/packages/rminer/index.html
- ROCR - 可視化評分分類器的性能. 官網:http://cran.r-project.org/web/packages/ROCR/index.html
- RoughSets - 使用粗糙集和模糊粗糙集理論進行數據分析. 官網:http://cran.r-project.org/web/packages/RoughSets/index.html
- rpart - Recursive Partitioning and Regression Trees. 官網:http://cran.r-project.org/web/packages/rpart/index.html
- RPMM - Recursively Partitioned Mixture Model. 官網:http://cran.r-project.org/web/packages/RPMM/index.html
- RSNNS - Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS). 官網:http://cran.r-project.org/web/packages/RSNNS/index.html
- Rsomoclu - Parallel implementation of self-organizing maps. 官網:https://cran.r-project.org/web/packages/Rsomoclu/index.html
- RWeka - Weka的R接口(Weka是基於JAVA環境下開源的機器學習以及數據挖掘軟件). 官網:http://cran.r-project.org/web/packages/RWeka/index.html
- RXshrink - RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression. 官網:http://cran.r-project.org/web/packages/RXshrink/index.html
- sda - Shrinkage Discriminant Analysis and CAT Score Variable Selection. 官網:http://cran.r-project.org/web/packages/sda/index.html
- SDDA - Stepwise Diagonal Discriminant Analysis. 官網:http://cran.r-project.org/web/packages/SDDA/index.html
- SuperLearner and subsemble - Multi-algorithm ensemble learning packages. 官網:https://github.com/ecpolley/SuperLearner
- svmpath - svmpath: the SVM Path algorithm. 官網:http://cran.r-project.org/web/packages/svmpath/index.html
- tgp - Bayesian treed Gaussian process models. 官網:http://cran.r-project.org/web/packages/tgp/index.html
- tree - 分類和迴歸樹. 官網:http://cran.r-project.org/web/packages/tree/index.html
- varSelRF - 使用隨機森林進行變量選擇. 官網:http://cran.r-project.org/web/packages/varSelRF/index.html
- xgboost - eXtreme Gradient Boosting Tree model, well known for its speed and performance. 官網:https://github.com/tqchen/xgboost/tree/master/R-package
13.天然語言處理
14.貝葉斯
15.最優化
16.金融
17.生物信息學
18.網絡分析
19.R 開發
20.日誌
21.數據包
22.其餘工具
23.其餘編譯器
24.R學習
25.資源
25.1 網站
25.2 書籍
25.3 博客
25.4 參考文獻
25.5 網絡課程
25.6 列表
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