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Operating systems: Ubuntu 14.04 LTS / Windows

IDE: R Studio

Databases: PostgreSQL / MySQL / SQLite

S.No. | Libraries in R | Type | Description |
---|---|---|---|

1 | ggplot2, googleVis, corrplot, lattice, ggfortify,ggrepel, ggalt, ggtree,ggtech, ggplot2 Extensions, rgl, Cairo,extrafont, showtext,animation, gganimate, misc3D, xkcd,imager,hrbrthemes, waffle, dendextend, r2d3, Patchwork | Data Visualization | Visualizing the graphs with the scales and layers, combining multiple plots, and visualizing the complex position of the plots using mathematical operators |

2 | plyr,dplyr, data.table, lubridate, reshape2,readr, haven, tidyr, broom, rlist, jsonlite, ff, stringi,stringr, bigmemory, fuzzyjoin, tidyverse | Data Manipulation | Supporting consistent, fast, and portable text processing and handling the complex data formats such as data-time and time-spans |

3 | MissForest | Missing Value Imputations | Imputing the mixed type of data such as continuous and/or categorical data in parallel manner |

4 | MissMDA | Missing Value Imputations | Handling missing values over large and complex datasets with multivariate analysis |

5 | Outliers | Outlier Detection | Providing a set of tests and functions to detect outliers |

6 | Extreme Values in R (EVIR) | Outlier Detection | Estimating extreme quantiles using several functions such as block maxima, exploratory data analysis, peak over thresholds, gev/gpd distributions, and point processes |

7 | Features | Feature Selection | Extracting the features such as mean value, local maxima and minima, first and second derivatives, noise and so on from discretely-sampled functional data |

8 | Regularized Random Forest (RRF) | Feature Selection | Selecting the features based on the random forest |

9 | FactoMineR | Feature Selection | Providing exploratory data analysis methods include Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA), hierarchical cluster analysis, and multiple factor analysis |

10 | Canonical Correlation Analysis (CCA) | Feature Selection | Performing significance test such as Monte Carlo and asymptotic tests |

11 | Companion to Applied Regression (CAR) | Continuous regression | Making type II and type III Anova tables using its Anova function |

12 | RandomForest | Classification, Regression | Creating a large number of decision trees for regression and classification and assessing proximities among the data values in unsupervised model |

13 | RMiner | Ordinal regression | Supporting the process of data mining classification and regression methods |

14 | CoreLearn | Ordinal regression | Providing a set of classification, regression, and feature evaluation methods to process the dataset having ordinal features |

15 | Classification And REgression Training (Caret) | Classification, Regression | Creating the predictive models and optimizing the process through a set of functions |

16 | BigRF | Classification, Regression | Handling a very large datasets using random forest algorithms Building multiple random forests in parallel to effectively process too large datasets |

17 | Clustering for Business Analytics (CBA) | Clustering | Manipulates data and performs efficient computation of cross distances with the help of Proximus and rock, and utility functions |

18 | RankCluster | Clustering | Ranking multivariate data through model-based clustering |

19 | forecast | Time Series | Forecasting from time series models or time series based on the class of the first argument |

20 | Linear Time Series Analysis (LTSA) | Time Series | Modeling linear time series for simulation, forecasting, and loglikelihood computation |

21 | survival | Survival Analysis | Predicting the time at which the occurrence of a particular event by creating survival object among the variables |

22 | Basta | Survival Analysis | Estimating the unknown birth and death times, survival trends, and age-specific mortality through multiple Markov Chain Monte Carlo (MCMC) simulations for large number of records having unknown birth and death times |

23 | Least-Squares Means (LSMeans) | General Model Validation | Computing least-squares means for many generalized linear, linear, and mixed models |

24 | Comparison | General Model Validation | Comparing a model object with the comparison object for validation |

25 | RegTest | Regression Validation | Conducting regression test for funnel plot asymmetry for ‘Rma’ objects |

26 | ACD | Regression Validation | Analyzing categorical data with missing or complete responses |

27 | BinomTools | Classification Validation | Performing diagnostics for binomial regression models using a set of diagnostic methods |

28 | DAIM | Classification Validation | Evaluating the classification accuracy through performance measures include sensitivity, AUC, specificity, bootstrap estimation, and repeated k-fold cross validation |

29 | ClustEval | Clustering Validation | Evaluating the clustering, individual clusters, and clustering algorithms |

30 | SigClust | Clustering Validation | Assessing the significance of the clustering algorithms using statistical method |

31 | PROC | Clustering Validation | Computing confidence interval for partial Receiver Operating Characteristic (ROC) curves based on the comparison with statistical tests |

32 | TimeROC | Clustering Validation | Estimating dynamic or cumulative time-dependent ROC curve |

33 | plotly, ggvis, DataTables, rCharts, heatmaply,d3heatmap, DiagrammeR, dygraphs, formattable, Leaflet,
MetricsGraphics, networkD3, scatterD3, rbokeh, threejs, timevis, visNetwork, wordcloud2, highcharter |
HTML Widgets | Providing interface to visualize the data in the form of plotsOffering numerous chart types with a simple syntax |

34 | knitr, rmarkdown, slidify, tinytex,xtable, rapport, Sweave, texreg, checkpoint, brew,ReporteRs, bookdown, ezknitr, drake | Reproducible research | Supporting the conversion of various formats and reproducible report templates |

35 | mlr | Machine learning | Providing a set of classification and regression techniquesComprising generic resampling, filter and wrapper methods, hyper parameter tuning methods and so on |

36 | eXtreme Gradient Boosting package (Xgboost) | Learning and Prediction | Supporting, regression, classification, and ranking objective functions |

37 | gbm | Regression Methods | Supporting generalized boosted regression modeling and performing an optimal number of iterations through out-of-BA estimator |

38 | Prophet | Time Series | Forecasting time series data based on the non-linear trends and handling outliers, missing data, and shifts in trends |

39 | Quality Control Chart (QCC) | Quality Control | Plotting Opearional Characteristic (OC) curve, Pareto chart, multivariate charts, cause-and-effect chart, and shewhart chart for attribute, count, and continuous data |

40 | shiny, shinyjs, RCurl, curl,httr, httpuv, XML, rvest, OpenCPU, Rfacebook,RSiteCatalyst, plumber | Web technologies and Services | Providing interface to client for easily accessing web pages |

41 | Parallel, Rmpi, future, SparkR,DistributedR, ddR, sparklyr, batchtools | Parallel Computing | Providing parallel and interactive computing environment |

42 | Rcpp, Rcpp11, compiler | High performance | Providing integration between different programming languages |

43 | rJava, jvmr, rJython, rPython,runr, RJulia, JuliaCall, RinRuby, R.matlab,RcppOctave, RSPerl, V8, htmlwidgets, rpy2 | Language API | Providing interface to other programming languages |

44 | RODBC, DBI, elastic, mongolite,odbc, RMariaDB, RMySQL, ROracle, RPostgreSQL,RSQLite, RJDBC, rmongodb, rredis, RCassandra,RHive, RNeo4j, rpostgis | Database Management | Providing interface for accessing the database |

45 | AnomalyDetection, ahaz, arules, bigrf, bigRR,bmrm, Boruta, BreakoutDetection, bst, CausalImpact, C50,caret, CORElearn, CoxBoost, Cubist, e1071, earth,elasticnet, ElemStatLearn, evtree, forecast, forecastHybrid,prophet, FSelector, frbs, GAMBoost, gamboostLSS, gbm,glmnet, glmpath, GMMBoost, grplasso, grpreg, h2o, hda,
ipred, kernlab, klaR, kohonen, lars, lasso2, LiblineaR,ime4, LogicReg, maptree, mboost, mlr, mvpart, MXNet, ncvreg, nnet, oblique.tree, pamr, party, party.kit, penalized,penalizedLDA, penalizedSVM, quantregForest, randomForest, randomSRC, ranger, rattle, rda, rdetools, REEMtree, relaxo,rgenoud, rgp, Rmalschains, rminer, ROCR, RoughSets, rpart, RPMM, RSNNS, Rsomoclu, RWeka, RXshrink, sda, SDDA, SuperLearner,subsemble, svmpath, tgp, tree, varSelRef, xgboost |
ML | Learning high dimensional and large-scale dataAnalyzing, manipulating, and representing the patterns and transaction data |

46 | text2vec, tm, openNLP, koRpus, zipfR, NLP,LDAvis, topicmodels, syuzhet, SnowballC, quanteda, MonkeyLearn,tidytext, utf8 | Natural Language Processing | Analyzing a set of documents using text mining toolsSupporting natural language text processing in different languages |

47 | coda, mcmc, MCMCpack, R2WinBUGS, BRugs, rjags, rstan | Bayesian | Providing interface for bayesian analysis |

48 | IpSolve, minqa, nloptr, ompr, Rglpk, ROL | Optimization | Resolving optimization problems include integer, linear, mixed integer, transportation, and assignment problems |

49 | qantmod, TTR, PerformanceAnalytics, zoo, xts, tseries,fAssets | Finance | Building technical trading rulesBuilding, trading, and analyzing quantitative financial trading strategies |

50 | Bioconductor, genetics, gap, ape, pheatmap | Bioinformatics and Biostatistics | Offering control over appearance and dimensionsAnalyzing genetic data and evolution |

51 | Igraph, network, sna, netdiffuseR, networkDynamic,ndtv, statnet, ergm, latentnet, tnet, rgext, visNetwork | Network Analysis | Visualizing the network data and handling the large graphs efficiently through statistical analysis |

52 | magick, imager | Image Processing | Supporting different image manipulations and a variety of image formatsProcessing the images up to four dimensions in a fast manner |