Rpackages.R

# Essential R packages for Data Science ----------------------------------------
# Author: Giovanni Zurlo (github: @zurlog)
# Repository: https://github.com/zurlog/R-Essentials
# Date: 2023-03-16
# Licence: GNU General Public License v3.0 

# R is a programming language and environment used for statistical computing and graphics.
# It is available for free from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/.

# To install the latest version of R, visit https://cran.r-project.org/ and click on the "Download R" link
# for your operating system. Follow the installation instructions provided.

# RStudio is an integrated development environment (IDE) for R that provides a user-friendly interface
# for writing and running R code. It is available for free from https://www.rstudio.com/products/rstudio/download/.

# To install the latest version of RStudio, visit https://www.rstudio.com/products/rstudio/download/,
# select the appropriate version for your operating system, and follow the installation instructions provided.


## CLEANING DATA ================================================================

install.packages(c("janitor","outliers","missForest","frequency","Amelia",
                   "diffobj","mice","VIM","Bioconductor","mi",
                   "wrangle"), dependencies = TRUE)

## janitor: Simple tools for data cleaning
## outliers: Detect outliers in data sets
## missForest: Impute missing values using the random forest algorithm
## frequency: Easy frequency tables from data sets
## Amelia: Multiple imputation of missing data
## diffobj: Compute and visualize differences of R objects
## mice: Multivariate imputation by chained equations
## VIM: Visualization and imputation of missing values
## Bioconductor: Tools for the analysis and comprehension of high-throughput genomic data
## mi: Tools for handling missing data in R
## wrangle: A collection of tools for data manipulation



## DATA TYPES AND FORMATS ========================================================

install.packages(c("stringr","lubridate","glue",
                   "scales","hablar","readr","readxl","haven"), dependencies = TRUE)

## stringr: String manipulation package
## lubridate: Work with dates and times in R
## glue: Glue strings together in a flexible way
## scales: Graphical scales map data to aesthetics in plots
## hablar: Convert natural language to logical expressions in R
## readr: Read rectangular text data from file or string
## readxl: Read Excel files (.xls and .xlsx) into R
## haven: Import and export 'SPSS', 'Stata' and 'SAS' files


## WRANGLING, SUBSETTING ========================================================

install.packages(c("tidyverse", "data.table"), dependencies = TRUE)

## tidyverse: A collection of packages for data manipulation and visualization
## - ggplot2: A system for creating graphics in R
## - dplyr: A grammar of data manipulation
## - tidyr: Tidy messy data
## - readr: A fast and friendly way to read rectangular data
## - purrr: Functional programming tools
## - tibble: A modern re-imagining of the data.frame
## - stringr: A consistent, simple and easy-to-use set of wrappers around the 
##   stringr functions of stringi package
## - forcats: Tools for working with categorical variables (factors)
##
## data.table: Fast and efficient data manipulation using data.table syntax




## STATISTICAL TESTS ============================================================

install.packages(c("stats","ggpubr","lme4","MASS","car"), 
                 dependencies = TRUE)


## SAMPLING ====================================================================

install.packages(c("sampling","icarus","sampler","SamplingStrata",
                   "survey","laeken","stratification","simPop"), 
                 dependencies = TRUE)


## MULTIVARIATE ANALYSIS =======================================================

install.packages(c("psych","CCA","CCP","MASS","icapca","gvlma","smacof",
                   "MVN","rpca","gpca","EFA.MRFA","MFAg","MVar","fabMix",
                   "fad","spBFA","cate","mnlfa","CSFA","GFA","lmds","SPCALDA",
                   "semds", "superMDS", "vcd", "vcdExtra"), 
                 dependencies = TRUE)


## CLASSIFICATION AND CLUSTERING ===============================================

install.packages(c("fpc","cluster","treeClust","e1071","NbClust","skmeans",
                   "kml","compHclust","protoclust","pvclust","genie", "tclust",
                   "ClusterR","dbscan","CEC","GMCM","EMCluster","randomLCA",
                   "MOCCA","factoextra",'poLCA', 'pdfCluster','flexclust',
                   "EMMIXskew","teigen"), dependencies = TRUE)

## fpc: Flexible Procedures for Clustering
## cluster: Cluster Analysis Extended Rousseeuw et al.
## treeClust: Cluster analysis with trees
## e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
## NbClust: Determining the Best Number of Clusters in a Data Set
## skmeans: Spherical k-Means Clustering
## kml: K-Means for Longitudinal Data
## compHclust: Complementary Hierarchical Clustering Analysis
## protoclust: Hierarchical Clustering with Prototypes
## pvclust: Hierarchical Clustering with P-Values via Multiscale Bootstrap Resampling
## genie: genie: A New, Fast, and Outlier Resistant Hierarchical Clustering Algorithm
## tclust: Robust Trimmed Clustering
## ClusterR: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering
## dbscan: Density-Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms
## CEC: Cross-Entropy Clustering
## GMCM: Generalized Maximum Contrast Method
## EMCluster: EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution
## randomLCA: Random Effects Latent Class Analysis
## MOCCA: Multi-Objective Optimization for Collecting Cluster Analysis Results
## factoextra: Extract and Visualize the Results of Multivariate Data Analyses
## poLCA: Polytomous variable Latent Class Analysis
## pdfCluster: Cluster analysis via nonparametric density estimation
## flexclust: Flexible Cluster Algorithms
## EMMIXskew: The EM Algorithm and Skew-Elliptical Distributions
## teigen: Model-Based Clustering and Dimension Reduction for the Wrapped Normal Distribution


# install.packages(".../EMMIXskew_1.0.3.tar.gz", repos = NULL, type = "source")

install.packages(c("rpart", "tree", "C50", "RWeka","klar", "e1071",
                   "kernlab","svmpath","superml","sboost"), 
                 dependencies = TRUE)


## TIME SERIES =================================================================

install.packages(c("ts","zoo","xts","timeSeries","tsModel", "TSMining",
                   "TSA","fma","fpp2","fpp3","tsfa","TSdist","TSclust","feasts",
                   "MTS", "dse","sazedR","kza","fable","forecast","tseries",
                   "nnfor","quantmod",'meboot','rugarch','betategarch','GAS'), dependencies = TRUE)

## ts: Time Series analysis and computation
## zoo: S3 Infrastructure for Regular and Irregular Time Series
## xts: Extensible time series
## timeSeries: Rmetrics - Financial Time Series Objects
## tsModel: Time Series Modeling for Air Pollution and Health
## TSMining: Mining Univariate and Multivariate Motifs in Time-Series Data
## TSA: Time Series Analysis
## fma: Data Sets from Forecasting: Methods and Applications
## fpp2: Data for "Forecasting: Principles and Practice" (2nd Edition)
## fpp3: Data for "Forecasting: Principles and Practice" (3rd Edition)
## tsfa: Time Series Factor Analysis
## TSdist: Distance Measures for Time Series Data
## TSclust: Time Series Clustering Utilities
## feasts: Feature Extraction And Statistics for Time Series
## MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models
## dse: Dynamic Systems Estimation (Time Series Package)
## sazedR: Statistical Analysis of Ziggurat Method (R)
## kza: Kolmogorov-Zurbenko Adaptive Filters
## fable: Forecasting Models for Tidy Time Series
## forecast: Forecasting functions for time series and linear models
## tseries: Time Series Analysis and Computational Finance
## nnfor: Time Series Forecasting with Neural Networks
## quantmod: Quantitative Financial Modelling Framework
## meboot: Maximum Entropy Bootstrap for Time Series
## rugarch: Univariate GARCH models
## betategarch: Beta-t-EGARCH models
## GAS: Generalized Autoregressive Score Models


## FUNCTIONAL DATA ==========================================================

install.packages(c("fda","FDboost","fds","ftsa","fdasrvf","refund","fdapace",
                   "StatFda","tidyfun","fdatest","fdakma","fdaMixed","goffda","mlr","fda.usc"))

## fda It is a basic reference to work in R with functional data
## FDboost: Boosting Functional Regression Models
## fds: Functional Data Sets
## ftsa: Functional Time Series Analysis
## fdasrvf: Elastic Functional Data Analysis
## refund: Regression with Functional Data
## fdapace: Functional Data Analysis and Empirical Dynamics
## StatFda: exploratory analysis and functional regression models
## tidyfun: makes data wrangling and exploratory analysis of functional data easier
## fdatest: Interval Testing Procedure for Functional Data
## fdakma: Functional Data Analysis: K-Mean Alignment
## fdaMixed: Functional data analysis in a mixed model framework
## goffda: Goodness-of-Fit Tests for Functional Data

 
## BUILDING AND VALIDATING ML ==================================================

install.packages(c("tree", "e1071","crossval","caret","rpart","bcv",
                   "klaR","EnsembleCV","gencve","cvAUC","CVThresh",
                   "cvTools","dcv","cvms","blockCV"), dependencies = TRUE)

## tree: Decision tree models
## e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien
## crossval: Generic functions for cross validation
## caret: Classification and Regression Training package
## rpart: Recursive Partitioning and Regression Trees
## bcv: Cross-Validation for the SVD (Bi-Cross-Validation)
## klaR: Classification and visualization
## EnsembleCV: Extensible package for Cross-validation-Based Integration of Base Learners
## gencve: General Cross Validation Engine
## cvAUC: Cross-validated area under the curve (AUC) for the ROC curve
## CVThresh: Estimating the optimal threshold for a prediction model with cross-validation
## cvTools: Tools for cross-validation in R
## dcv: Cross-Validation for Discriminant Analysis
## cvms: Cross-Validation for Model Selection
## blockCV: Block-wise Cross-Validation for Covariate-Adjusted Models



## RANDOM FORESTS ==============================================================

install.packages(c("randomForest","grf","ipred","party","randomForestSRC",
                   "grf","BART","Boruta","LTRCtrees","REEMtree","refr",
                   "binomialRF","superml"), dependencies = TRUE)

## randomForest: Classification and regression based on a forest of trees using random inputs
## grf: Generalized random forest for classification, regression and survival analysis
## ipred: Improved predictive models using random forests
## party: A laboratory for recursive partytioning
## randomForestSRC: Random Forests for survival, regression and classification (RF-SRC)
## grf: Generalized random forest for classification, regression and survival analysis
## BART: Bayesian Additive Regression Trees
## Boruta: Wrapper algorithm for all-relevant feature selection
## LTRCtrees: Survival trees to model left-truncated and right-censored data
## REEMtree: Regression trees with random effects for longitudinal (panel) data
## refr: An implementation of the Randomized Ensembles Framework
## binomialRF: Binomial random forest for imbalanced data
## superml: Build machine learning models as easily as a formula


## MODEL INTERPRETATION =====================================================

install.packages(c("lime","localModel","iml","EIX","flashlight",
                   "interpret","outliertree","breakDown"), 
                 dependencies = TRUE)

## Lime: Machine learning explanation and model interpretation package
## localModel: Fit local regression models to non-linear data
## iml: Interpretable Machine Learning package
## EIX: Explainability of Importance in Random Forest
## flashlight: Model Agnostic Feature Selection
## interpret: Fit interpretable models and explain blackbox models
## outliertree: Identify and visualize outliers in regression trees
## breakDown: Model-agnostic methods for decomposition of prediction results


## DOCUMENTS CREATION ==========================================================

install.packages(c("devtools","usethis","roxygen2","knitr",
                   "rmarkdown","flexdashboard","Shiny",
                   "xtable","httr","profvis","officedown"), dependencies = TRUE)

## devtools: Tools to make developing R packages easier
## usethis: Automate package and project setup tasks
## roxygen2: In-line documentation for R
## knitr: A general-purpose package for dynamic report generation in R
## rmarkdown: Dynamic documents for R
## flexdashboard: Easy interactive dashboards for R
## Shiny: Web application framework for R
## xtable: Export tables to LaTeX or HTML
## httr: Tools for working with URLs and HTTP
## profvis: Interactive visualizations for profiling R code
## officedown: Convert R Markdown to Office documents (Word, PowerPoint and Excel)


## BAYESIAN INFERENCE =======================================================

options(mc.cores = parallel::detectCores())

install.packages(c("rstan","brms","rstanarm","tidybayes","bayestestR",
                   "bayesrules","bayesplot","loo"), dependencies = T)


## rstan: Provides R interface to the Stan probabilistic programming language for Bayesian inference.
## brms: Implements Bayesian regression models using Stan for fitting, estimation, and posterior prediction.
## rstanarm: Implements Bayesian regression models using Stan for fitting, estimation, and posterior prediction, but with a simplified R syntax.
## tidybayes: Provides tidy data structures and visualization tools for Bayesian models using ggplot2.
## bayestestR: Provides various tools for Bayesian model checking, hypothesis testing, and model comparison.
## bayesrules: Implements Bayesian networks for modeling probabilistic relationships between variables.
## bayesplot: Provides plotting functions for visualizing Bayesian inference results.
## loo: Implements the leave-one-out cross-validation method for comparing models and assessing model fit.


## TIDY MODELLING ===========================================================

install.packages("tidymodels")

## rsamples: split the data into training and testing sets (as well as cross validation sets)
## recipes: prepare the data with preprocessing (assign variables and preprocessing steps)
## parsnip: specify and fit the data to a model 
## yardstick and tune: evaluate model performance by metrics
## workflows: combining recipe and parsnip objects into a workflow (this makes it easier to keep track of what you have done and it makes it easier to modify specific steps)
## tune and dials: model optimization (hyperparameters)
## broom: make the output from fitting a model easier to read
## baguette: speeding up bagging pipelines
## butcher: dealing with pipelines that create model objects that take up too much memory
## discrim: more model options for classification
## embed: extra preprocessing options for categorical predictors
## corrr: more options for looking at correlation matrices
## rules: more model options for prediction rule ensembles
## tidypredict: running predictions inside SQL databases
## modeldb: working within SQL databases and it allows for dplyr and tidyeval use within a database
## tidyposterior: compares models using resampling statistics


## GAMs ========================================================================

install.packages(c("gam","mgcv","vgam","gamlss","mboost","gss","scam","gamm4",
                   "bayesx"), dependencies = TRUE)

## gam: Generalized Additive Models for regression with the original backfitting approach *
## mgcv: Mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation 
## vgam: Vector Generalized Linear and Additive Models *
##     - pospois and posnegbinom functions in the VGAM package fit truncated poisson and negbin models
## gamlss: Generalized Additive Models for location, scale and shape **
## mboost: Model-based boosting
## gss: General smoothing splines
## scam: Shape constrained additive models
## gamm4: Generalized Additive Mixed Models using 'mgcv' and 'lme4'
## bayesx: Bayesian inference in structured additive regression models ***
## Methods discussed here are in R recommended package mgcv

## *No smoothing parameter selection
## **Limited smoothing parameter selection
## ***see also mgcv::jagam


## NETWORK MODELS ==============================================================

install.packages(c("igraph", "igraphdata", "networkdata", "blockmodels",
                   "latentnet", "huge", "covglasso"), dependencies = TRUE)


## igraph: Network analysis and visualization package
## igraphdata: Example datasets for the 'igraph' package
## networkdata: Example datasets for network analysis
## blockmodels: Fit and analyze block models for networks
## latentnet: Latent position and cluster models for network analysis
## huge: High-dimensional undirected graph estimation
## covglasso: Gaussian graphical models with the lasso penalty


## NLP ========================================================================

install.packages(c("tm", "quanteda", "tidytext", "wordcloud", "topicmodels",
                   "SentimentAnalysis", "text2vec", "syuzhet", "openNLP",
                   "openNLPdata", "NLP", "RWeka", "tau", "koRpus", "stringr",
                   "tokenizers", "textmineR", "rvest", "hunspell", "lexicon"), dependencies = TRUE)


## tm: Text mining tools for managing, processing, and analyzing text data
## quanteda: Quantitative text analysis methods and utilities
## tidytext: Text mining using tidy data principles
## wordcloud: Creating word cloud visualizations
## topicmodels: Fitting and analyzing topic models for text data
## SentimentAnalysis: Analyzing sentiment in text data
## text2vec: Efficient text vectorization and topic modeling
## syuzhet: Sentiment and emotion analysis using narrative arcs
## openNLP: Natural language processing with the Apache OpenNLP tools
## openNLPdata: Model files required for the 'openNLP' library
## NLP: Basic classes and methods for natural language processing
## RWeka: R interface to the Weka machine learning toolkit, including text classification algorithms
## tau: Text analysis utilities
## koRpus: Analyzing and processing text data, including readability measures
## stringr: Simple and consistent manipulation of strings
## tokenizers: Various text tokenization methods
## textmineR: Text mining and topic modeling
## rvest: Web scraping, including text extraction from HTML
## hunspell: Spell checking and stemming based on the Hunspell library
## lexicon: Collection of lexicons and dictionaries for text analysis