RSS POSTS: ##
- A simple test of the martingale hypothesis in esgtoolkit
- Kerning and Kerning in a Widening Gyre
- New version of package gDefrag
- Rlinguo — Why Did We Build It?
- zigg 0.0.2 on CRAN: Micromaintenance
- RcppArmadillo 14.2.3-1 on CRAN: Small Upstream Fix
- RcppUUID 1.1.2 on CRAN: Newly Adopted Package
- Key considerations for retiring/superseding an R package
- Designing monochrome data visualisations
- Compiling Programs in Linux: A Beginner’s Step-by-Step Guide
- How to Combine Lists in R: A Complete Guide with Examples
- How to Append Values to List in R: A Complete Guide with Examples
- How to Append Values to a Vector Using a Loop in R: A Comprehensive Guide
- 3MW (Save Data at AWS S3 With {paws})
- Building LLM-Powered Applications with Shiny for Python: Practical Insights
- Scaling Decision Support Systems: When to Use React, Python, and R
- How a GxP Audit Will Help You Go Through FDA and EMA Submission
- How to dynamically aggregate any dataset in R with purrr and dplyr
- Simpson’s Paradox in a Logistic Regression
CRANberries UPDATED:
- {string2path} 0.2.0: Rendering Font into ‘data.frame’ - diffify
- {HH} 3.1-53: Statistical Analysis and Data Display: Heiberger and Holland - diffify
- {tidyHeatmap} 1.11.6: A Tidy Implementation of Heatmap - diffify
- {RLescalation} 1.0.2: Optimal Dose Escalation Using Deep Reinforcement Learning - diffify
- {openrouteservice} 0.6.2: An ‘openrouteservice’ API Client - diffify
- {rbioapi} 0.8.2: User-Friendly R Interface to Biologic Web Services’ API - diffify
- {itscalledsoccer} 0.3.1: American Soccer Analysis API Client - diffify
- {renv} 1.1.1: Project Environments - diffify
- {DQAgui} 0.2.6: Graphical User Interface for Data Quality Assessment - diffify
- {clidamonger} 1.3.0: Monthly Climate Data for Germany, Usable for Heating and Cooling
Calculations - diffify
- {SynergyLMM} 1.0.1: Statistical Framework for in Vivo Drug Combination Studies - diffify
- {StepReg} 1.5.8: Stepwise Regression Analysis - diffify
- {DQAstats} 0.3.7: Core Functions for Data Quality Assessment - diffify
- {aberrance} 0.2.0: Detect Aberrant Behavior in Test Data - diffify
- {vol2birdR} 1.0.6: Vertical Profiles of Biological Signals in Weather Radar Data - diffify
- {rjwsacruncher} 0.2.1: Interface to the ‘JWSACruncher’ of ‘JDemetra+’ - diffify
- {cccrm} 3.0.4: Concordance Correlation Coefficient for Repeated (and
Non-Repeated) Measures - diffify
- {arrApply} 2.2.1: Apply a Function to a Margin of an Array - diffify
- {zigg} 0.0.2: Lightweight Interfaces to the ‘Ziggurat’ Pseudo Random Number
Generator - diffify
- {rbmi} 1.4.0: Reference Based Multiple Imputation - diffify
- {LatticeDesign} 3.0-1: Lattice-Based Space-Filling Designs - diffify
- {kollaR} 1.0.3: Filtering, Visualization and Analysis of Eye Tracking Data - diffify
- {campsismod} 1.2.0: Generic Implementation of a PK/PD Model - diffify
- {stevetemplates} 1.1.0: Steve’s R Markdown Templates - diffify
- {odin} 1.2.7: ODE Generation and Integration - diffify
- {pencal} 2.2.3: Penalized Regression Calibration (PRC) for the Dynamic
Prediction of Survival - diffify
- {GaussSuppression} 0.9.5: Tabular Data Suppression using Gaussian Elimination - diffify
- {ForestElementsR} 2.1.0: Data Structures and Functions for Working with Forest Data - diffify
- {faraway} 1.0.9: Datasets and Functions for Books by Julian Faraway - diffify
- {entropart} 1.6-16: Entropy Partitioning to Measure Diversity - diffify
- {cyphr} 1.1.7: High Level Encryption Wrappers - diffify
- {synthesizer} 0.4.0: Synthesize Data Based on Empirical Quantile Functions and Rank
Order Matching - diffify
- {paws.common} 0.8.0: Paws Low-Level Amazon Web Services API - diffify
- {jsonvalidate} 1.5.0: Validate ‘JSON’ Schema - diffify
- {duckplyr} 1.0.0: A ‘DuckDB’-Backed Version of ‘dplyr’ - diffify
- {klassR} 1.0.2: Classifications and Codelists for Statistics Norway - diffify
- {mirai} 2.1.0: Minimalist Async Evaluation Framework for R - diffify
- {bayestestR} 0.15.2: Understand and Describe Bayesian Models and Posterior
Distributions - diffify
- {report} 0.6.1: Automated Reporting of Results and Statistical Models - diffify
- {rayrender} 0.38.8: Build and Raytrace 3D Scenes - diffify
- {mlr3torch} 0.2.0: Deep Learning with ‘mlr3’ - diffify
- {centr} 0.2.2: Weighted and Unweighted Spatial Centers - diffify
- {bsitar} 0.3.2: Bayesian Super Imposition by Translation and Rotation Growth
Curve Analysis - diffify
- {emayili} 0.9.3: Send Email Messages - diffify
- {stoppingrule} 0.5.2: Create and Evaluate Stopping Rules for Safety Monitoring - diffify
- {galah} 2.1.1: Biodiversity Data from the GBIF Node Network - diffify
- {bndesr} 1.0.4: Access Data from the Brazilian Development Bank (BNDES) - diffify
- {SomaDataIO} 6.2.0: Input/Output ‘SomaScan’ Data - diffify
- {geostatsp} 2.0.8: Geostatistical Modelling with Likelihood and Bayes - diffify
- {ellmer} 0.1.1: Chat with Large Language Models - diffify
- {sreg} 1.0.1: Stratified Randomized Experiments - diffify
- {aws.wrfsmn} 0.0.5: Data Processing of SMN Hi-Res Weather Forecast from ‘AWS’ - diffify
- {checkglobals} 0.1.3: Static Analysis of R-Code Dependencies - diffify
- {adegenet} 2.1.11: Exploratory Analysis of Genetic and Genomic Data - diffify
- {TOSTER} 0.8.4: Two One-Sided Tests (TOST) Equivalence Testing - diffify
- {easylabel} 0.3.3: Interactive Scatter Plot and Volcano Plot Labels - diffify
- {trtswitch} 0.1.3: Treatment Switching - diffify
- {arcpullr} 0.3.0: Pull Data from an ‘ArcGIS REST’ API - diffify
- {sate} 2.3.0: Scientific Analysis of Trial Errors (SATE) - diffify
- {easystats} 0.7.4: Framework for Easy Statistical Modeling, Visualization, and
Reporting - diffify
- {BayesianMCPMod} 1.0.2: Simulate, Evaluate, and Analyze Dose Finding Trials with
Bayesian MCPMod - diffify
- {vistla} 2.1.0: Detecting Influence Paths with Information Theory - diffify
- {dibble} 0.3.1: Dimensional Data Frames - diffify
- {TreatmentPatterns} 3.0.0: Analyzes Real-World Treatment Patterns of a Study Population of
Interest - diffify
- {SLOS} 1.0.1: ICU Length of Stay Prediction and Efficiency Evaluation - diffify
- {urlparse} 0.2.0: Fast Simple URL Parser - diffify
- {insight} 1.0.2: Easy Access to Model Information for Various Model Objects - diffify
- {Coxmos} 1.1.1: Cox MultiBlock Survival - diffify
- {bvhar} 2.2.0: Bayesian Vector Heterogeneous Autoregressive Modeling - diffify
- {ggalign} 0.1.0: A ‘ggplot2’ Extension for Consistent Axis Alignment - diffify
- {convertid} 0.1.10: Convert Gene IDs Between Each Other and Fetch Annotations from
Biomart - diffify
- {VGAMdata} 1.1-13: Data Supporting the ‘VGAM’ Package - diffify
- {DrugExposureDiagnostics} 1.1.1: Diagnostics for OMOP Common Data Model Drug Records - diffify
- {ChainLadder} 0.2.20: Statistical Methods and Models for Claims Reserving in General
Insurance - diffify
- {windex} 2.1.0: Analysing Convergent Evolution using the Wheatsheaf Index - diffify
- {sgs} 0.3.4: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control - diffify
- {netmeta} 3.1-1: Network Meta-Analysis using Frequentist Methods - diffify
- {dfr} 0.1.4: Dual Feature Reduction for SGL - diffify
- {CvmortalityMult} 1.0.8: Cross-Validation for Multi-Population Mortality Models - diffify
- {bamdit} 3.4.4: Bayesian Meta-Analysis of Diagnostic Test Data - diffify
- {deductive} 1.0.1: Data Correction and Imputation Using Deductive Methods - diffify
- {RcppArmadillo} 14.2.3-1: ‘Rcpp’ Integration for the ‘Armadillo’ Templated Linear Algebra
Library - diffify
- {SPAS} 2025.2.1: Stratified-Petersen Analysis System - diffify
- {gemini.R} 0.8.0: Interface for ‘Google Gemini’ API - diffify
- {leidenbase} 0.1.32: R and C/C++ Wrappers to Run the Leiden find_partition() Function - diffify
- {Lifertable} 0.1.0: Life and Fertility Tables Specially for Insects - diffify
- {ggeffects} 2.2.0: Create Tidy Data Frames of Marginal Effects for ‘ggplot’ from
Model Outputs - diffify
- {signnet} 1.0.5: Methods to Analyse Signed Networks - diffify
- {rstanemax} 0.1.8: Emax Model Analysis with ‘Stan’ - diffify
- {tinyplot} 0.3.0: Lightweight Extension of the Base R Graphics System - diffify
- {geomorph} 4.0.10: Geometric Morphometric Analyses of 2D and 3D Landmark Data - diffify
- {espadon} 1.10.0: Easy Study of Patient DICOM Data in Oncology - diffify
- {BKT} 0.1.0: Bayesian Knowledge Tracing Model - diffify
- {ReturnCurves} 1.0.1: Estimation of Return Curves - diffify
- {purrr} 1.0.4: Functional Programming Tools - diffify
- {paleobuddy} 1.1.0: Simulating Diversification Dynamics - diffify
- {crew.aws.batch} 0.0.8: A Crew Launcher Plugin for AWS Batch - diffify
- {twoStageDesignTMLE} 1.0.1.2: Targeted Maximum Likelihood Estimation for Two-Stage Study
Design - diffify
- {transfR} 1.1.0: Transfer of Hydrograph from Gauged to Ungauged Catchments - diffify
- {PlotFTIR} 1.1.0: Plot FTIR Spectra - diffify
- {NMsim} 0.1.6: Seamless ‘Nonmem’ Simulation Platform - diffify
- {GaussianHMM1d} 1.1.2: Inference, Goodness-of-Fit and Forecast for Univariate Gaussian
Hidden Markov Models - diffify
- {crew.cluster} 0.3.4: Crew Launcher Plugins for Traditional High-Performance Computing
Clusters - diffify
- {bspline} 2.3.0: B-Spline Interpolation and Regression - diffify
- {sessioninfo} 1.2.3: R Session Information - diffify
- {GWnnegPCA} 0.0.5: Geographically Weighted Non-Negative Principal Components
Analysis - diffify
- {fishmethods} 1.13-1: Fishery Science Methods and Models - diffify
- {DAISIE} 4.5.0: Dynamical Assembly of Islands by Speciation, Immigration and
Extinction - diffify
- {ympes} 1.8.0: Collection of Helper Functions - diffify
- {PhenotypeR} 0.1.2: Assess Study Cohorts Using a Common Data Model - diffify
- {HVT} 25.2.2: Constructing Hierarchical Voronoi Tessellations and Overlay
Heatmaps for Data Analysis - diffify
- {grates} 1.4.1: Grouped Date Classes - diffify
- {boiwsa} 1.1.3: Seasonal Adjustment of Weekly Data - diffify
- {aifeducation} 1.0.2: Artificial Intelligence for Education - diffify
- {super} 0.0.5: Interpreted String Literals - diffify
- {statgenIBD} 1.0.8: Calculation of IBD Probabilities - diffify
- {SmallCountRounding} 1.2.0: Small Count Rounding of Tabular Data - diffify
- {shinyscholar} 0.2.5: A Template for Creating Reproducible ‘shiny’ Applications - diffify
- {scplot} 0.5.0: Plot Function for Single-Case Data Frames - diffify
- {scan} 0.62.0: Single-Case Data Analyses for Single and Multiple Baseline
Designs - diffify
- {netrankr} 1.2.4: Analyzing Partial Rankings in Networks - diffify
- {modelbased} 0.9.0: Estimation of Model-Based Predictions, Contrasts and Means - diffify
- {archive} 1.1.11: Multi-Format Archive and Compression Support - diffify
- {pqrBayes} 1.0.5: Bayesian Penalized Quantile Regression - diffify
- {nanostringr} 0.5.0: Performs Quality Control, Data Normalization, and Batch Effect
Correction for ‘NanoString nCounter’ Data - diffify
- {jskm} 0.5.10: Kaplan-Meier Plot with ‘ggplot2’ - diffify
- {correctR} 0.3.1: Corrected Test Statistics for Comparing Machine Learning Models
on Correlated Samples - diffify
- {diceR} 3.0.0: Diverse Cluster Ensemble in R - diffify
- {EpiNow2} 1.7.0: Estimate Real-Time Case Counts and Time-Varying Epidemiological
Parameters - diffify
- {ClassComparison} 3.3.5: Classes and Methods for “Class Comparison” Problems on
Microarrays - diffify
- {AhoCorasickTrie} 0.1.3: Fast Searching for Multiple Keywords in Multiple Texts - diffify
- {Umpire} 2.0.11: Simulating Realistic Gene Expression and Clinical Data - diffify
- {multilevLCA} 2.0.0: Estimates and Plots Single-Level and Multilevel Latent Class
Models - diffify
- {metadat} 1.4-0: Meta-Analysis Datasets - diffify
- {posologyr} 1.2.8: Individual Dose Optimization using Population Pharmacokinetics - diffify
- {plotthis} 0.5.1: High-Level Plotting Built Upon ‘ggplot2’ and Other Plotting
Packages - diffify
- {MixtureMissing} 3.0.4: Robust and Flexible Model-Based Clustering for Data Sets with
Missing Values at Random - diffify
- {gmoTree} 1.4.1: Get and Modify ‘oTree’ Data - diffify
- {constrainedKriging} 0.2-11: Constrained, Covariance-Matching Constrained and Universal Point
or Block Kriging - diffify
- {QuadratiK} 1.1.3: Collection of Methods Constructed using Kernel-Based Quadratic
Distances - diffify
- {metasnf} 2.0.0: Meta Clustering with Similarity Network Fusion - diffify
- {FIESTA} 3.7.0: Forest Inventory Estimation and Analysis - diffify
- {irboost} 0.2-1.0: Iteratively Reweighted Boosting for Robust Analysis - diffify
- {SSBtools} 1.7.0: Statistics Norway’s Miscellaneous Tools - diffify
- {ggbreak} 0.1.4: Set Axis Break for ‘ggplot2’ - diffify
- {rollupTree} 0.2.0: Perform Recursive Computations - diffify
- {joineRML} 0.4.7: Joint Modelling of Multivariate Longitudinal Data and
Time-to-Event Outcomes - diffify
- {gstat} 2.1-3: Spatial and Spatio-Temporal Geostatistical Modelling, Prediction
and Simulation - diffify
- {fbnet} 1.0.4: Forensic Bayesian Networks - diffify
- {RSAGA} 1.4.2: SAGA Geoprocessing and Terrain Analysis - diffify
- {forensIT} 1.1.1: Information Theory Tools for Forensic Analysis - diffify
- {BMisc} 1.4.8: Miscellaneous Functions for Panel Data, Quantiles, and Printing
Results - diffify
- {ssifs} 1.0.4: Stochastic Search Inconsistency Factor Selection - diffify
- {RoBMA} 3.4.0: Robust Bayesian Meta-Analyses - diffify
- {maptiles} 0.9.0: Download and Display Map Tiles - diffify
- {primes} 1.6.1: Fast Functions for Prime Numbers - diffify
- {singleRcapture} 0.2.2: Single-Source Capture-Recapture Models - diffify
- {ForestTools} 1.0.3: Tools for Analyzing Remote Sensing Forest Data - diffify
- {ggpca} 0.1.3: Publication-Ready PCA, t-SNE, and UMAP Plots - diffify
- {dbi.table} 1.0.3: Database Queries Using ‘data.table’ Syntax - diffify
- {cmhc} 0.2.10: Access, Retrieve, and Work with CMHC Data - diffify
- {areaplot} 2.1.3: Plot Stacked Areas and Confidence Bands as Filled Polygons - diffify
- {teal.slice} 0.6.0: Filter Module for ‘teal’ Applications - diffify
- {miceFast} 0.8.5: Fast Imputations Using ‘Rcpp’ and ‘Armadillo’ - diffify
- {strata} 1.4.0: Simple Framework for Simple Automation - diffify
- {tidyterra} 0.7.0: ‘tidyverse’ Methods and ‘ggplot2’ Helpers for ‘terra’ Objects - diffify
- {rgrass} 0.5-1: Interface Between ‘GRASS’ Geographical Information System and
‘R’ - diffify
- {ordbetareg} 0.8: Ordered Beta Regression Models with ‘brms’ - diffify
- {cgmanalysis} 3.0.2: Clean and Analyze Continuous Glucose Monitor Data - diffify
- {SWMPr} 2.5.2: Retrieving, Organizing, and Analyzing Estuary Monitoring Data - diffify
- {sommer} 4.3.7: Solving Mixed Model Equations in R - diffify
- {RcppAlgos} 2.9.3: High Performance Tools for Combinatorics and Computational
Mathematics - diffify
- {interfacer} 0.3.3: Define and Enforce Contracts for Dataframes as Function
Parameters - diffify
- {aebdata} 0.1.4: Access Data from the Atlas do Estado Brasileiro - diffify
- {torch} 0.14.1: Tensors and Neural Networks with ‘GPU’ Acceleration - diffify
- {smooth} 4.1.1: Forecasting Using State Space Models - diffify
- {RegSDC} 1.0.0: Information Preserving Regression-Based Tools for Statistical
Disclosure Control - diffify
- {locfit} 1.5-9.11: Local Regression, Likelihood and Density Estimation - diffify
- {mongolite} 3.0.1: Fast and Simple ‘MongoDB’ Client for R - diffify
- {lessR} 4.4.1: Less Code, More Results - diffify
- {crew} 1.0.0: A Distributed Worker Launcher Framework - diffify
- {TSLSTMplus} 1.0.6: Long-Short Term Memory for Time-Series Forecasting, Enhanced - diffify
- {optRF} 1.1.0: Optimising Random Forest Stability by Determining the Optimal
Number of Trees - diffify
- {openssl} 2.3.2: Toolkit for Encryption, Signatures and Certificates Based on
OpenSSL - diffify
- {SCDB} 0.5.0: Easily Access and Maintain Time-Based Versioned Data
(Slowly-Changing-Dimension) - diffify
- {lcmm} 2.2.0: Extended Mixed Models Using Latent Classes and Latent Processes - diffify
- {RStoolbox} 1.0.2.1: Remote Sensing Data Analysis - diffify
- {poweRlaw} 1.0.0: Analysis of Heavy Tailed Distributions - diffify
- {mltest} 1.0.3: Classification Evaluation Metrics - diffify
- {datacutr} 0.2.3: SDTM Datacut - diffify
- {rayvertex} 0.12.0: 3D Software Rasterizer - diffify
- {petersenlab} 1.1.0: A Collection of R Functions by the Petersen Lab - diffify
- {ocf} 1.0.3: Ordered Correlation Forest - diffify
- {BiostatsUHNplus} 1.0.2: Nested Data Summary, Adverse Events and REDCap - diffify
- {quickcode} 1.0.5: Quick and Essential ‘R’ Tricks for Better Scripts - diffify
CRANberries NEW:
- {SVDMx} 0.1.0: Child/Child-Adult Mortality-Indexed Model Mortality Age
Schedules
- {gofigR} 0.2.2: Client for ‘GoFigr.io’
- {CUtools} 0.1.0: Clinical Utility Tools to Analyze a Predictive Model
- {polymatching} 1.0.1: A Matching Algorithm for Designs with Multiple Groups
- {kollaR} 1.0.3: Filtering, Visualization and Analysis of Eye Tracking Data
- {taxize} 0.10.0: Taxonomic Information from Around the Web
- {stochtree} 0.1.0: Stochastic Tree Ensembles (XBART and BART) for Supervised
Learning and Causal Inference
- {saery} 2.0: Small Area Estimation for Rao and Yu Model
- {MIC} 1.0.2: Analysis of Antimicrobial Minimum Inhibitory Concentration Data
- {cogirt} 1.0.0: Cognitive Testing Using Item Response Theory
- {CIDER} 0.99.4: Meta-Clustering for scRNA-Seq Integration and Evaluation
- {shapr} 1.0.2: Prediction Explanation with Dependence-Aware Shapley Values
- {params} 0.7.7: Simplify Parameters
- {MBNMAdose} 0.5.0: Dose-Response MBNMA Models
- {sphereML} 0.1.0: Analyzing Students’ Performance Dataset in Physics Education
Research (SPHERE) using Machine Learning (ML)
- {tall} 0.1.0: Text Analysis for All
- {oneinfl} 1.0.0: Estimates OIPP and OIZTNB Regression Models
- {jellyfisher} 1.0.4: Visualize Spatiotemporal Tumor Evolution with Jellyfish Plots
- {EEAaq} 1.0.0: Handle Air Quality Data from the European Environment Agency
Data Portal
- {DiscreteDLM} 1.0.0: Bayesian Distributed Lag Model Fitting for Binary and Count
Response Data
- {watcher} 0.1.0: Watch the File System for Changes
- {SynergyLMM} 1.0.1: Statistical Framework for in Vivo Drug Combination Studies
- {pseudoCure} 1.0.0: A Pseudo-Observations Approach for Analyzing Survival Data with
a Cure Fraction
- {WpProj} 0.2.3: Linear p-Wasserstein Projections
- {SPIChanges} 0.1.0: Improves the Interpretation of the Standardized Precipitation
Index Under Changing Climate Conditions
- {matriz} 1.0.1: Literature Matrix Synthesis Tools for Epidemiology and Health
Science Research
- {Horsekicks} 1.0.2: Provide Extensions to the Prussian Army Death by Horsekick Data
- {CircNNTSRSymmetric} 0.1.0: Circular Data using Symmetric NNTS Models
- {BayesAT} 0.1.0: Bayesian Adaptive Trial
- {SurvMetrics} 0.5.1: Predictive Evaluation Metrics in Survival Analysis
- {tergo} 0.1.8: Style Your Code Fast
- {SQLFormatteR} 0.0.1: Format SQL Queries
- {shiny2docker} 0.0.1: Generate Dockerfiles for ‘Shiny’ Applications
- {rmcmc} 0.1.1: Robust Markov Chain Monte Carlo Methods
- {MOsemiind} 0.1.0: Marshall-Olkin Shock Models with Semi-Independent Time
- {kpiwidget} 0.1.0: KPI Widgets for Quarto Dashboards with Crosstalk
- {RFplus} 1.2-2: Progressive Bias Correction of Satellite Environmental Data
- {nlpembeds} 1.0.0: Natural Language Processing Embeddings
- {fastQR} 1.0.0: Fast QR Decomposition and Update
- {shinyr} 0.4.2: Data Insights Through Inbuilt R Shiny App
- {tmap.cartogram} 0.1: Extension to ‘tmap’ for Creating Cartograms
- {shinykanban} 0.0.1: Create Kanban Board in Shiny Applications
- {authoritative} 0.1.0: Parse and Deduplicate Author Names
- {raybevel} 0.2.2: Generates Polygon Straight Skeletons and 3D Bevels
- {lightAUC} 0.1.1: Fast AUC Computation
- {HetSeq} 0.1.0: Identifying Modulators of Cellular Responses Leveraging
Intercellular Heterogeneity
- {legion} 0.2.1: Forecasting Using Multivariate Models
- {MplusLGM} 1.0.0: Automate Latent Growth Mixture Modelling in ‘Mplus’
- {writer} 0.1.0: Write from Multiple Sources to a Database Table
- {resumiR} 1.0.2: Medidas Resumen y Tablas de Frecuencia para Datos Numéricos /
Summary Measures and Frequency Tables for Numerical Data
- {OPL} 1.0.0: Optimal Policy Learning
- {gimap} 1.0.1: Calculate Genetic Interactions for Paired CRISPR Targets
- {autoimport} 0.1.1: Automatic Generation of @importFrom Tags
- {timeplyr} 1.0.0: Fast Tidy Tools for Date and Date-Time Manipulation
- {enrichR} 3.4: Provides an R Interface to ‘Enrichr’