R Weekly 2021-W02 fastai, renv, Github Actions

This article is originally published at

Hello and welcome to this new issue!

• How to have (my) content shared by R Weekly?

This week’s release was curated by Batool Almarzouq, with help from the R Weekly team members and contributors.

Highlight

• Automatic Rendering of a Plot with GitHub Actions

• {fastai} 2.0.2: Interface to fastai

• {renv} 0.12.5: Project Environments

Insights

• Using the bootstrap for bias reduction

• rOpenSci 2020 Code of Conduct Transparency Report

• rOpenSci Code of Conduct Annual Review

• COVID-19 Data: The Long Run

• Lilliefors, Kolmogorov-Smirnov and cross-validation

• Coming soon: effortlessly generate ordinal data without assuming proportional odds

• Bringing R to Swift on macOS

• Most popular posts for 2020

• This is how you create a one year long newspaper

R in the Real World

• Predicting the outcome of the English Premier League by using Monte Carlo Method (in R)

• IMDb datasets: 3 centuries of movie rankings visualized

• Exploring the game “First Orchard” with simulation in R

• F1 2020 Season Review

• Predicting injuries for Chicago traffic crashes

R in Organizations

• Professional Financial Reports with RMarkdown

R in Academia

• Finding Economic Articles with Data and Specific Empirical Methods

Tutorials

• Rtini Part II: Vectorize the Absolute $#!+ Out of This

• Automatic Rendering of a Plot with GitHub Actions

• Safety Checking Locally Installed Package URLs

• Portable Continuous Integration using r-ci

• Smoothing isn’t Always Safe

• Isovists using uniform ray casting in R

• Custom Google Analytics Dashboards with R: Building The Dashboard

• The Nature of Overfitting

• Little useless-useful R functions – Playing stack of cards

• Little useless-useful R functions – Mathematical puzzle of Four fours

• Tutorial: Adding Open Street Map Data to Rayshader Maps in R

• Create a simple R data package during a coffee break in 7 steps

• A guide to dynamically show a light/dark plot in a website based on the user’s preference, using ggplot2 and pkgdown

Resources

• future.BatchJobs - End-of-Life Announcement

New Packages

???? Go Live for More New Pkgs ????

CRAN

• {knitcitations} 1.0.12: Citations for ‘Knitr’ Markdown Files

• {HydroMe} 2.0-1: Estimating Water Retention and Infiltration Model Parameters using Experimental Data

• {bdlp} 0.9-2: Transparent and Reproducible Artificial Data Generation

• {aptg} 0.1.1: Automatic Phylogenetic Tree Generator

• {TukeyRegion} 0.1.4: Tukey Region and Median

• {simPH} 1.3.13: Simulate and Plot Estimates from Cox Proportional Hazards Models

• {mortyr} 0.0.2: Wrapper to ‘The Rick and Morty’ API

• {scrappy} 0.0.1: A Simple Web Scraper

• {msaeDB} 0.1.0: Difference Benchmarking for Multivariate Small Area Estimation

• {metaggR} 0.1.0: Calculate the Knowledge-Weighted Estimate

• {MARSSVRhybrid} 0.1.0: MARS SVR Hybrid

• {geostats} 1.0: An Introduction to Statistics for Geoscientists

• {ctsemOMX} 1.0.3: Continuous Time SEM - ‘OpenMx’ Based Functions

• {covid19jp} 0.1.0: Japanese Covid-19 Datasets

• {QuadRoot} 0.1.0: Quadratic Root for any Quadratic Equation

• {EMDSVRhybrid} 0.1.0: Hybrid Machine Learning Model

• {convergEU} 0.5.1: Monitoring Convergence of EU Countries

• {APCI} 0.1.0: A New Age-Period-Cohort Model for Describing and Investigating Inter-Cohort Differences and Life Course Dynamics

• {nbapalettes} 0.1.0: An NBA Jersey Palette Generator

• {tabulator} 1.0.0: Efficient Tabulation with Stata-Like Output

• {tablet} 0.2.0: Tabulate Descriptive Statistics in Multiple Formats

• {read.gt3x} 1.0.1: Parse ‘Actigraph’ ‘GT3X’/’GT3X+’ ‘Accelerometer’ Data

• {matrixdist} 1.0: Statistics for Matrix Distributions

• {mactivate} 0.6.4: Multiplicative Activation

• {RandomForestsGLS} 0.1.0: Random Forests for Dependent Data

• {KPC} 0.1.0: Kernel Partial Correlation Coefficient

• {GBcurves} 0.1.0: Yield Curves of Brazil, China, and Russia

• {eatRep} 0.13.4: Educational Assessment Tools for Replication Methods

• {CauchyCP} 0.1.0: Powerful Test for Survival Data under Non-Proportional Hazards

• {vimpclust} 0.1.0: Variable Importance in Clustering

• {sfcr} 0.1.1: Simulate Stock-Flow Consistent Models

• {mgss} 1.0: A Matrix-Free Multigrid Preconditioner for Spline Smoothing

• {cragg} 0.0.1: Tests for Weak Instruments in R

• {Rcurvep} 1.2.0: Concentration-Response Data Analysis using Curvep

• {peopleanalyticsdata} 0.1.0: Data Sets for Keith McNulty’s Handbook of Regression Modeling in People Analytics

• {VARMER} 0.1.0: Variational Merging

• {PEACH} 0.1.1: Pareto Enrichment Analysis for Combining Heterogeneous Datasets

• {PheNorm} 0.1.0: Unsupervised Gold-Standard Label Free Phenotyping Algorithm for EHR Data

• {nmm} 0.9: Nonlinear Multivariate Models

• {ccid} 1.0.0: Cross-Covariance Isolate Detect: a New Change-Point Method for estimating Dynamic Functional Connectivity

• {UPG} 0.2.2: Efficient Bayesian Models for Binary and Categorical Data

• {collapse} 1.5.0: Advanced and Fast Data Transformation

• {footprint} 0.1: Calculate Air Travel Emissions

• {SAMGEP} 0.1.0-1: A Semi-Supervised Method for Prediction of Phenotype Event Times

• {ProSGPV} 0.1.0: Penalized Regression with Second-Generation P-Values

• {noisyr} 0.1.0: Noise Quantification in High Throughput Sequencing Output

• {braidReports} 0.5.4: Visualize Combined Action Response Surfaces and Report BRAID Analyses

• {excelstrippr} 0.1.2: Extracts Tabular Data from Excel Reports

• {WSGeometry} 1.0: Compute Wasserstein Barycenters, Geodesics, PCA and Distances

• {proceduralnames} 0.1.1: Several Methods for Procedural Name Generation

• {ggmulti} 0.1.0: High Dimensional Data Visualization

• {FuzzyQ} 0.1.0: Fuzzy Quantification of Common and Rare Species

• {disastr.api} 1.0.3: Wrapper for the UN OCHA ReliefWeb Disaster Events API

• {BED} 1.4.3: Biological Entity Dictionary (BED)

• {arabic2kansuji} 0.1.0: Convert Arabic Numerals to Kansuji

• {pkgnews} 0.0.1: Retrieve R Package News Files

• {Crossover} 0.1-19: Analysis and Search of Crossover Designs

• {fdm2id} 0.9.5: Data Mining and R Programming for Beginners

GitHub or Bitbucket

• {gibboda}: Gender Isn’t Binary But Other Data Are.

• {vegadown}: ‘knitr’ Engine to Render ‘YAML’ and ‘JSON’ into ‘Vega’.

• {quicknews}: Some R-functions for navigating the online news landscape.]

Updated Packages

• {rotasym} 1.0.9: Tests for Rotational Symmetry on the Hypersphere

• {flashlight} 0.7.3: Shed Light on Black Box Machine Learning Models

• {fad} 0.2-1: Factor Analysis for Data

• {addinslist} 0.3: Discover and Install Useful RStudio Addins

• {SphericalCubature} 1.5: Numerical Integration over Spheres and Balls in n-Dimensions; Multivariate Polar Coordinates

• {spatialEco} 1.3-5: Spatial Analysis and Modelling Utilities

• {circlize} 0.4.12: Circular Visualization

• {shiny.semantic} 0.4.2: Semantic UI Support for Shiny

• {scoringTools} 0.1.2: Credit Scoring Tools

• {rgl} 0.104.16: 3D Visualization Using OpenGL

• {RcppArmadillo} 0.10.1.2.2: ‘Rcpp’ Integration for the ‘Armadillo’ Templated Linear Algebra Library

• {MVar.pt} 2.1.4: Analise multivariada (brazilian portuguese)

• {MFAg} 1.8: Multiple Factor Analysis (MFA)

• {markovchain} 0.8.5-4: Easy Handling Discrete Time Markov Chains

• {git2r} 0.28.0: Provides Access to Git Repositories

• {DMCfun} 1.3.0: Diffusion Model of Conflict (DMC) in Reaction Time Tasks

• {ctmm} 0.6.0: Continuous-Time Movement Modeling

• {bayesplot} 1.8.0: Plotting for Bayesian Models

• {WVPlots} 1.3.2: Common Plots for Analysis

• {clustcurv} 2.0.1: Determining Groups in Multiples Curves

• {tsfa} 2021.1-3: Time Series Factor Analysis

• {TestDimorph} 0.4.0: Analysis Of The Interpopulation Difference In Degree of Sexual Dimorphism Using Summary Statistics

• {Pursuit} 1.0.2: Projection Pursuit

• {PCAmatchR} 0.3.0: Match Cases to Controls Based on Genotype Principal Components

• {oysteR} 0.1.1: Scans R Projects for Vulnerable Third Party Dependencies

• {MVar} 2.1.4: Multivariate Analysis

• {maximin} 1.0-4: Space-Filling Design under Maximin Distance

• {kayadata} 0.5.0: Kaya Identity Data for Nations and Regions

• {ivsacim} 1.1: Structural Additive Cumulative Intensity Models with IV

• {faoutlier} 0.7.6: Influential Case Detection Methods for Factor Analysis and Structural Equation Models

• {epinetr} 0.94: Epistatic Network Modelling with Forward-Time Simulation

• {cmfrec} 2.4.2: Collective Matrix Factorization for Recommender Systems

• {buildr} 0.1.0: Organize & Run Build Scripts Comfortably

• {bcmaps} 1.0.0: Map Layers and Spatial Utilities for British Columbia

• {ARDL} 0.1.1: ARDL, ECM and Bounds-Test for Cointegration

• {sparsestep} 1.0.1: SparseStep Regression

• {ralger} 2.2.1: Easy Web Scraping

• {plotly} 4.9.3: Create Interactive Web Graphics via plotly.js

• {treemapify} 2.5.5: Draw Treemaps in ggplot2

• {piecepackr} 1.6.5: Board Game Graphics

• {eye} 1.0.1: Analysis of Eye Data

• {SSLR} 0.9.3.1: Semi-Supervised Classification, Regression and Clustering Methods

• {sp} 1.4-5: Classes and Methods for Spatial Data

• {prettydoc} 0.4.1: Creating Pretty Documents from R Markdown

• {GWpcor} 0.1.6: Geographically Weighted Partial Correlation Coefficient

• {AssocTests} 1.0-1: Genetic Association Studies

• {sysfonts} 0.8.3: Loading Fonts into R

• {lessR} 3.9.9: Less Code, More Results

• {threeBrain} 0.1.9: 3D Brain Visualization

• {showtext} 0.9-2: Using Fonts More Easily in R Graphs

• {recosystem} 0.4.4: Recommender System using Matrix Factorization

• {DirStats} 0.1.7: Nonparametric Methods for Directional Data

• {dipsaus} 0.1.2: A Dipping Sauce for Data Analysis and Visualizations

• {adehabitatHR} 0.4.19: Home Range Estimation

• {MPV} 1.56: Data Sets from Montgomery, Peck and Vining

• {starsExtra} 0.2.2: Miscellaneous Functions for Working with ‘stars’ Rasters

• {assignR} 2.0.0: Infer Geographic Origin from Isotopic Data

• {OpenRepGrid.ic} 0.5.0: Interpretive Clustering for Repertory Grids

• {swfscAirDAS} 0.2.2: Southwest Fisheries Science Center Aerial DAS Data Processing

• {arctools} 1.1.0: Processing and Physical Activity Summaries of Minute Level Activity Data

• {imsig} 1.1.3: Immune Cell Gene Signatures for Profiling the Microenvironment of Solid Tumours

• {sjstats} 0.18.1: Collection of Convenient Functions for Common Statistical Computations

• {multinma} 0.2.1: Bayesian Network Meta-Analysis of Individual and Aggregate Data

• {fastai} 2.0.2: Interface to fastai

• {fabricatr} 0.12.0: Imagine Your Data Before You Collect It

• {Directional} 4.6: A Collection of R Functions for Directional Data Analysis

• {Ball} 1.3.10: Statistical Inference and Sure Independence Screening via Ball Statistics

• {leaflet} 2.0.4.1: Create Interactive Web Maps with the JavaScript ‘Leaflet’Library

• {TSPred} 5.0: Functions for Benchmarking Time Series Prediction

• {renv} 0.12.5: Project Environments

• {pcalg} 2.7-1: Methods for Graphical Models and Causal Inference

• {iCAMP} 1.3.4: Infer Community Assembly Mechanisms by Phylogenetic-Bin-Based Null Model Analysis

• {highfrequency} 0.8.0: Tools for Highfrequency Data Analysis

• {blogdown} 1.0: Create Blogs and Websites with R Markdown

• {vizdraws} 1.1: Visualize Draws from the Prior and Posterior Distributions

• {vimp} 2.1.6: Perform Inference on Algorithm-Agnostic Variable Importance

• {gtsummary} 1.3.6: Presentation-Ready Data Summary and Analytic Result Tables

• {tsvr} 1.0.2: Timescale-Specific Variance Ratio for Use in Community Ecology

• {MXM} 1.5.0: Feature Selection (Including Multiple Solutions) and Bayesian Networks

• {MSEtool} 3.0.0: Management Strategy Evaluation Toolkit

• {ipmisc} 5.0.2: Miscellaneous Functions for Data Cleaning and Analysis

• {SampleSize4ClinicalTrials} 0.2.3: Sample Size Calculation for the Comparison of Means or Proportions in Phase III Clinical Trials

• {moderndive} 0.5.1: Tidyverse-Friendly Introductory Linear Regression

• {BayesPostEst} 0.3.1: Generate Postestimation Quantities for Bayesian MCMC Estimation

• {BayesSPsurv} 0.1.2: Bayesian Spatial Split Population Survival Model

• {poismf} 0.2.7: Factorization of Sparse Counts Matrices Through Poisson Likelihood

• {MarketMatching} 1.2.0: Market Matching and Causal Impact Inference

• {elevatr} 0.3.3: Access Elevation Data from Various APIs

• {valaddin} 1.0.1: Functional Input Validation

• {stratamatch} 0.1.6: Stratification and Matching for Large Observational Data Sets

• {ProjectTemplate} 0.10.0: Automates the Creation of New Statistical Analysis Projects

• {ggeasy} 0.1.3: Easy Access to ‘ggplot2’ Commands

• {profileModel} 0.6.1: Profiling Inference Functions for Various Model Classes

• {nofrills} 0.3.1: Low-Cost Anonymous Functions

• {momentuHMM} 1.5.2: Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

• {fflr} 0.3.15: Collect ESPN Fantasy Football Data

• {ufs} 0.4.1: Quantitative Analysis Made Accessible

• {Tplyr} 0.3.1: A Grammar of Clinical Data Summary

• {splithalf} 0.7.2: Calculate Task Split Half Reliability Estimates

• {compositions} 2.0-1: Compositional Data Analysis

• {W2CWM2C} 2.2: A Graphical Tool for Wavelet (Cross) Correlation and Wavelet Multiple (Cross) Correlation Analysis

• {RSQLite} 2.2.2: ‘SQLite’ Interface for R

• {RcppBigIntAlgos} 1.0.1: Factor Big Integers with the Parallel Quadratic Sieve

• {r5r} 0.3-2: Rapid Realistic Routing with R5

• {corr2D} 1.0.2: Implementation of 2D Correlation Analysis in R

• {copCAR} 2.0-4: Fitting the copCAR Regression Model for Discrete Areal Data

• {act} 1.0: Aligned Corpus Toolkit

• {SoilHyP} 0.1.5: Soil Hydraulic Properties

• {readsdr} 0.2.0: Translate Models from System Dynamics Software into ‘R’

• {ggridges} 0.5.3: Ridgeline Plots in ggplot2

• {segregation} 0.4.0: Entropy-Based Segregation Indices

• {support.CEs} 0.5-0: Basic Functions for Supporting an Implementation of Choice Experiments

• {speedglm} 0.3-3: Fitting Linear and Generalized Linear Models to Large Data Sets

• {powerSurvEpi} 0.1.1: Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies

• {greybox} 0.6.6: Toolbox for Model Building and Forecasting

• {metaBMA} 0.6.6: Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

• {eplusr} 0.14.0: A Toolkit for Using Whole Building Simulation Program ‘EnergyPlus’

• {TreeBUGS} 1.4.7: Hierarchical Multinomial Processing Tree Modeling

• {pkgndep} 1.0.2: Check the Heaviness of Package Dependencies

• {moonBook} 0.2.4: Functions and Datasets for the Book by Keon-Woong Moon

• {genieclust} 0.9.8: The Genie++ Hierarchical Clustering Algorithm with Noise Points Detection

• {forestinventory} 1.0.0: Design-Based Global and Small-Area Estimations for Multiphase Forest Inventories

• {sigminer} 1.2.1: Extract, Analyze and Visualize Mutational Signatures for Genomic Variations

• {RIA} 1.4.3: Radiomics Image Analysis Toolbox for Medial Images

• {intsurv} 0.2.2: Integrative Survival Modeling

• {hexbin} 1.28.2: Hexagonal Binning Routines

• {climate} 0.9.9: Interface to Download Meteorological (and Hydrological) Datasets

• {BayesMallows} 1.0.0: Bayesian Preference Learning with the Mallows Rank Model

• {magickGUI} 1.2.2: GUI Tools for Interactive Image Processing with magick

• {diffeqr} 1.1.0: Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)

• {spatstat.sparse} 1.2-0: Sparse Three-Dimensional Arrays and Linear Algebra Utilities

• {NST} 3.0.6: Normalized Stochasticity Ratio

• {brinton} 0.2.4: A Graphical EDA Tool

• {stplanr} 0.8.1: Sustainable Transport Planning

• {robmixglm} 1.2-1: Robust Generalized Linear Models (GLM) using Mixtures

• {rnoaa} 1.3.0: ‘NOAA’ Weather Data from R

• {PostcodesioR} 0.3.0: API Wrapper Around ‘Postcodes.io’

• {partition} 0.1.3: Agglomerative Partitioning Framework for Dimension Reduction

• {noise} 1.0.1: Estimation of Intrinsic and Extrinsic Noise from Single-Cell Data

• {jtools} 2.1.2: Analysis and Presentation of Social Scientific Data

• {GRPtests} 0.1.1: Goodness-of-Fit Tests in High-Dimensional GLMs

• {tidytext} 0.3.0: Text Mining using ‘dplyr’, ‘ggplot2’, and Other Tidy Tools

• {qtl} 1.47-9: Tools for Analyzing QTL Experiments

• {DRDID} 1.0.1: Doubly Robust Difference-in-Differences Estimators

• {difNLR} 1.3.7: DIF and DDF Detection by Non-Linear Regression Models

• {swfscDAS} 0.5.0: Southwest Fisheries Science Center Shipboard DAS Data Processing

• {spatstat.utils} 1.20-2: Utility Functions for ‘spatstat’

• {SpaDES.core} 1.0.5: Core Utilities for Developing and Running Spatially Explicit Discrete Event Models

• {nimbleSCR} 0.1.1: Spatial Capture-Recapture (SCR) Methods Using nimble

• {MSCquartets} 1.1.0: Analyzing Gene Tree Quartets under the Multi-Species Coalescent

• {IndexNumber} 1.3: Index Numbers in Social Sciences

• {formattable} 0.2.1: Create ‘Formattable’ Data Structures

• {BRISC} 1.0.0: Fast Inference for Large Spatial Datasets using BRISC

• {scdhlm} 0.5.2: Estimating Hierarchical Linear Models for Single-Case Designs

• {pubmed.mineR} 1.0.17: Text Mining of PubMed Abstracts

• {sen2r} 1.4.0: Find, Download and Process Sentinel-2 Data

• {renv} 0.12.5: Project Environments

• {pmc} 1.0.4: Phylogenetic Monte Carlo

• {modelStudio} 2.1.1: Interactive Studio for Explanatory Model Analysis

• {MCMCglmm} 2.30: MCMC Generalised Linear Mixed Models

• {FSelector} 0.32: Selecting Attributes

• {excelstrippr} 0.1.2: Extracts Tabular Data from Excel Reports

• {envi} 0.1.6: Environmental Interpolation using Spatial Kernel Density Estimation

• {BioInsight} 0.2.0: Filter and Plot RNA Biotypes

• {alfred} 0.1.10: Downloading Time Series from ALFRED Database for Various Vintages

• {wyz.code.metaTesting} 1.1.20: Wizardry Code Meta Testing

• {wellknown} 0.7.2: Convert Between ‘WKT’ and ‘GeoJSON’

• {gateR} 0.1.6: Flow/Mass Cytometry Gating via Spatial Kernel Density Estimation

• {ConcordanceTest} 1.0.0: An Alternative to the Kruskal-Wallis Based on the Kendall Tau Distance

• {modelsummary} 0.6.5: Summary Tables and Plots for Statistical Models and Data: Beautiful, Customizable, and Publication-Ready

• {varTestnlme} 1.0.0: Variance Components Testing for Linear and Nonlinear Mixed Effects Models

• {statgenGxE} 1.0.4: Genotype by Environment (GxE) Analysis

• {r5r} 0.3-2: Rapid Realistic Routing with ‘R5’

• {ptmixed} 1.0.2: Poisson-Tweedie Generalized Linear Mixed Model

• {prcbench} 0.9.1: Testing Workbench for Precision-Recall Curves

• {mcmcensemble} 2.1: Ensemble Sampler for Affine-Invariant MCMC

• {heatwaveR} 0.4.5: Detect Heatwaves and Cold-Spells

• {FFD} 1.0-8: Freedom from Disease

• {eye} 1.0.0: Analysis of Eye Data

• {EpiEstim} 2.2-4: Estimate Time Varying Reproduction Numbers from Epidemic Curves

• {dm} 0.1.10: Relational Data Models

• {disastr.api} 1.0.3: Wrapper for the UN OCHA ReliefWeb Disaster Events API

• {ClimProjDiags} 0.1.1: Set of Tools to Compute Various Climate Indices

• {bayesmix} 0.7-5: Bayesian Mixture Models with JAGS

• {SIMMS} 1.3.1: Subnetwork Integration for Multi-Modal Signatures

• {fabCI} 0.2: FAB Confidence Intervals

• {gmp} 0.6-2: Multiple Precision Arithmetic

• {genoPlotR} 0.8.11: Plot Publication-Grade Gene and Genome Maps

• {varSel} 0.2: Sequential Forward Floating Selection using Jeffries-Matusita Distance

• {sjmisc} 2.8.6: Data and Variable Transformation Functions

• {Bayesrel} 0.7.0.3: Bayesian Reliability Estimation

• {sfsmisc} 1.1-8: Utilities from ‘Seminar fuer Statistik’ ETH Zurich

• {natmanager} 0.4.7: Install the ‘Natverse’ Packages from Scratch

• {mixIndependR} 0.4.4: Genetics and Independence Testing of Mixed Genetic Panels

• {randomLCA} 1.1-0: Random Effects Latent Class Analysis

• {SHAPforxgboost} 0.1.0: SHAP Plots for ‘XGBoost’

• {nngeo} 0.4.1: k-Nearest Neighbor Join for Spatial Data

• {corpustools} 0.4.4: Managing, Querying and Analyzing Tokenized Text

• {Clustering} 1.7.2: Execution of Multiple Clustering Algorithm

• {singcar} 0.1.2: Comparing Single Cases to Small Samples

• {RPostgres} 1.3.0: ‘Rcpp’ Interface to ‘PostgreSQL’

• {onewaytests} 2.5: One-Way Tests in Independent Groups Designs

• {pedSimulate} 0.1.1: Pedigree, Genetic Merit and Phenotype Simulation

• {genieclust} 0.9.8: The Genie++ Hierarchical Clustering Algorithm with Noise Points Detection

• {splines2} 0.4.1: Regression Spline Functions and Classes

• {shinypanel} 0.1.4: Shiny Control Panel

• {s20x} 3.1-30: Functions for University of Auckland Course STATS 201/208 Data Analysis

• {RMariaDB} 1.1.0: Database Interface and ‘MariaDB’ Driver

• {rgrass7} 0.2-4: Interface Between GRASS 7 Geographical Information System and R

• {RCzechia} 1.6.3: Spatial Objects of the Czech Republic

• {RcppXsimd} 7.1.5: Xsimd C++ Header-Only Library Files

• {processR} 0.2.6: Implementation of the ‘PROCESS’ Macro

• {proceduralnames} 0.1.1: Several Methods for Procedural Name Generation

• {precrec} 0.12.0: Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics) Curves

• {logitnorm} 0.8.38: Functions for the Logitnormal Distribution

• {llama} 0.9.4: Leveraging Learning to Automatically Manage Algorithms

• {fakemake} 1.10.0: Mock the Unix Make Utility

• {estimatr} 0.30.0: Fast Estimators for Design-Based Inference

• {diffobj} 0.3.3: Diffs for R Objects

• {calculus} 0.3.0: High Dimensional Numerical and Symbolic Calculus

• {boot.heterogeneity} 1.1.2: A Bootstrap-Based Heterogeneity Test for Meta-Analysis

• {gggibbous} 0.1.1: Moon Charts, a Pie Chart Alternative

• {BNSP} 2.1.5: Bayesian Non- And Semi-Parametric Model Fitting

• {BioVenn} 1.1.1: Create Area-Proportional Venn Diagrams from Biological Lists

• {bigsparser} 0.4.1: Sparse Matrix Format with Data on Disk

• {StratifiedMedicine} 1.0.3: Stratified Medicine

• {radous} 0.1.1: Query Random User Data from the Random User Generator API

• {neat} 1.2.3: Efficient Network Enrichment Analysis Test

• {TSSS} 1.3.1: Time Series Analysis with State Space Model

• {TraitStats} 1.0.1: Statistical Data Analysis for Randomized Block Design Experiments

• {sars} 1.3.2: Fit and Compare Species-Area Relationship Models Using Multimodel Inference

• {SARP.moodle} 0.8.6: XML Output Functions for Easy Creation of Moodle Questions

• {MRReg} 0.1.3: MDL Multiresolution Linear Regression Framework

• {garma} 0.9.7: Fitting and Forecasting Gegenbauer ARMA Time Series Models

• {emba} 0.1.8: Ensemble Boolean Model Biomarker Analysis

• {dexterMST} 0.9.2: CML and Bayesian Calibration of Multistage Tests

• {outForest} 0.1.1: Multivariate Outlier Detection and Replacement

• {GPvam} 3.0-7: Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling

• {SlidingWindows} 0.1.9: Methods for Time Series Analysis

• {ibelief} 1.3.1: Belief Function Implementation

• {gbfs} 1.3.6: Interface with Live Bikeshare Data

• {spcosa} 0.3-10: Spatial Coverage Sampling and Random Sampling from Compact Geographical Strata

• {mully} 2.1.31: Create, Modify and Visualize Multi-Layered Networks

• {libr} 1.1.1: Libraries, Data Dictionaries, and a Data Step for R

• {lamW} 2.0.0: Lambert-W Function

• {fdrtool} 1.2.16: Estimation of (Local) False Discovery Rates and Higher Criticism

• {primer} 1.2.0: Functions and Data for the Book, a Primer of Ecology with R

• {jmcm} 0.2.3: Joint Mean-Covariance Models using ‘Armadillo’ and S4

• {nbTransmission} 1.1.2: Naive Bayes Transmission Analysis

• {MEDseq} 1.2.1: Mixtures of Exponential-Distance Models with Covariates

• {compareDF} 2.3.1: Do a Git Style Diff of the Rows Between Two Dataframes with Similar Structure

• {qs} 0.23.5: Quick Serialization of R Objects

• {nardl} 0.1.6: Nonlinear Cointegrating Autoregressive Distributed Lag Model

• {gdtools} 0.2.3: Utilities for Graphical Rendering

• {tsdb} 1.0-0: Terribly-Simple Data Base for Time Series

• {Matrix} 1.3-2: Sparse and Dense Matrix Classes and Methods

• {IFAA} 1.0.1: Robust Analysis for Absolute Abundance in Microbiome

• {corHMM} 2.6: Hidden Markov Models of Character Evolution

• {chillR} 0.72.2: Statistical Methods for Phenology Analysis in Temperate Fruit Trees

• {xfun} 0.20: Miscellaneous Functions by ‘Yihui Xie’

• {tools4uplift} 1.0.0: Tools for Uplift Modeling

Shiny Apps

• In Review: Your 2020 on Twitter

• 2020 in Headlines:app

• Soccer Analytics Library: Relevant research in the soccer analytics space.

• R Shiny {golem} - Designing the UI - Part 1 - Development to Production

• Creating a Shiny app for your stock portfolio

Videos and Podcasts

• Identifying Bottlenecks (slow code parts) in R using Profiling / profvis

• How to run 100 regressions without loops in R

• Daniel Aleman - The Key Metric for your Forecast is… TRUST

• Emil Hvitfeldt - palette2vec - A new way to explore color paletttes

• Why R Webinar - Integrating Rshiny and REDCap

• Kenneth Benoit - Why you should stop using other text mining packages and embrace quanteda

R Internationally

• (Japanese) “polite”で守るWebスクレイピングのエチケット

R Project Updates

Updates from R Core

• Last Call for the 2020 R Community Survey

Upcoming Events in 3 Months

Events in 3 Months:

• RStudio::Global (January 21, 2020)

• A list of R conferences and meetings

• This week’s local R-User and applied stats events

Call for Participation

Your support will keep R Weekly team moving! ????

Quotes of the Week

I <may> have gone overboard on my research getting ready to teach #Rstats to anthropology students pic.twitter.com/4q7sz9Kkc8

— Marc Kissel (@MarcKissel) January 9, 2021

Relationship goals!#RStats #Rladies #rladiestunis pic.twitter.com/Gy3gZLbQzs

— R-Ladies Tunis (@RLadiesTunis) January 10, 2021

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