One hundred ninety-seven new packages made it to CRAN in June. Here are my selections for the “Top 40” in ten categories: Computational Methods, Data, Finance, Genomics, Machine Learning, Medicine, Statistics, Time Series, Utilities, and Visualization. The Medicine category includes multiple packages for medical reporting and table building. Note that eight new packages were removed from CRAN by the time I began my research for this post on July 16th, so they were not considered for the “Top 40”.
disordR v0.0-2: Provides tools for manipulating values of associative maps which are stored in arbitrary order. When associating keys with values one needs both parts to be in 1-1 correspondence. See the vignette for the theory and examples.
ICvectorfields v0.0.2: Provides functions for converting time series of spatial abundance or density data in raster format to vector fields of population movement using the digital image correlation technique. See the vignette.
rim v0.4.1: Provides an interface to the computer algebra system Maxima which includes running Maxima commands from within R, generating output in LaTeX and MathML and R Markdown. Look here for examples.
PlanetNICFI v1.0.3: Provides functions to download and process Planet NICFI satellite imagery from Norway’s International Climate and Forest Initiative utilizing the Planet Mosaics API. See the vignette.
etrm v1.0.1: Provides functions to perform core tasks within Energy Trading and Risk Management including calculating maximum smoothness forward price curves for electricity and natural gas contracts with flow delivery, as presented in Benth et al. (2007) and portfolio insurance trading strategies for price risk management in the forward market as described in Black (1976). There are vignettes describing the Maximum Smoothness Forward Curve and Portfolio Insurance Trading Strategies.
mshap v0.1.0: Provides functions to compute mSHAP values on two-part models as proposed by Matthews & Hartman (2021) using the TreeSHAP algorithm described in Lundberg et al. (2020). See the vignettes mSHAP and mshap plots.
AutoScore v0.2.0: Implements an interpretable machine learning framework to automate the development of a clinical scoring model for predefined outcomes. See Xie et al. 2020 for the details, and the Guide Book to get started.
daiR v0.9.0: Implements an interface for the Google Cloud Services Document AI API with additional tools for output file parsing and text reconstruction. See the package website for more information and examples. There are six vignettes including a User Guide, Basic processing, and Extracting Tables.
luz v0.1.0: Implements a high level interface to torch providing utilities to reduce the the amount of code needed for common tasks, abstract away torch details and make the same code work on both CPUs and GPUs. See Howard et al. (2020) and Falcon et al. (2019) for background and the vignettes Get started with Luz, Custom Loops and Accelerator API.
mcboost v0.3.0: Implements Multi-Calibration Boosting and Multi-Accuracy Boosting to for the multi-calibrate the predictions of machine learning models. See the vignettes Basics and Extensions and Health Survey Example.
aldvmm v0.8.4: Fits health state utility adjusted limited dependent variable mixture models, i.e. finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. See Alava and Wailoo (2015) for background and the vignette for an example.
clinDataReview v1.1.0: Provides functions to create interactive tables, listings and figures and associated reports for exploratory analysis in a clinical trial setting. There are vignettes on Prerocessing, Visualization, and Creating Reports.
inTextSummaryTable v3.0.1: Provides functions to create tables of summary statistics or counts for clinical data for TLFs. These tables can be exported as in-text table for a Clinical Study Report in MS Word format or a presentation MS PowerPoint format, or as interactive table. There is an Introduction and six more vignettes including Aesthetics and Visualization.
patientProfilesVis v2.0.1: Provides functions to create patient specific profile visualizations for exploration, diagnostic or monitoring purposes during a clinical trial which display the evolution of parameters such as laboratory measurements, ECG data, vital signs, adverse events and more. There is template for creating patient profiles from CDISC SDTM datasets, and an Introduction to the package.
RHRT v1.0.1: Provides methods to scan RR interval data for Premature Ventricular Complexes and parameterise and plot the resulting Heart Rate Turbulence. See Schmidt et al. (1999) and Blesius et al. (2020) and the vignette for examples.
visR v0.2.0: Provides functions to generate clinical graphs and tables with sensible defaults based on graphical principles as described in: Vandemeulebroecke et al. (2018), Vandemeulebroecke et al. (2019), and Morris et al. (2019). Vignettes include Survival Analysis using CDISC ADaM standard, Creating Consort Flow Diagram, Styling Survival Plots and Survival Analysis.
admix v0.3.2: Implements several methods to estimate the unknown quantities related to two-component admixture models, where the two components can belong to any distribution. See Bordes & Vandekerkhove (2010), Patra & Sen (2016), and Milhaud et al. (2021) for background. There are vignettes on Clusterine, Estimation, and Hypothesis Testing.
ahMLE v1.18: Implements methods for fitting additive hazards model which include the maximum likelihood method as well as Aalen’s method for estimating the additive hazards model. See Chengyuan Lu(2021) for details and the vignette for an example.
dbglm v1.0.0: Provides a function to fit generalized linear models on moderately large datasets, by taking an initial sample, fitting in memory, then evaluating the score function for the full data in the database. See Lumley (2019) for the details.
flatness v0.1.4: Provides S3 classes, plotting functions, indices and tests to analyze the flatness of histograms including functions for flatness tests introduced in Jolliffe & Primo (2008), flatness indices described in Wilks (2019), and the procedure for multiple hypothesis described in Benjamini & Hochberg (1995). See the vignette for examples.
outlierensembles v0.1.0: Provides ensemble functions for detecting outliers and anomalies including a new method based on Item Response Theory described in Kandanaarachchi (2021) and methods described in Schubert et al. (2012), Chiang et al. (2017), and Aggarwal and Sathe (2015). See the vignette for examples.
susieR v0.11.42: Implements methods for variable selection in linear regression based on the Sum of Single Effects model, as described in Wang et al (2020). The Iterative Bayesian Stepwise Selection algorithm allows fitting models to large data sets with thousands of samples and hundreds of thousands of variables. There are ten short vignettes including Trend Fitting and a minimal example.
psdr v1.0.1: Provides functions to generate and compare power spectral density plots given time series data and to compare the dominant frequencies of multiple groups of time series. Look here and here for the mathematical background. For examples look here or see this vignette. There is also an Introduction.
archive v1.0.2: Implements bindings to libarchive, the multi-format archive and compression library which offer connections and direct extraction for many archive formats including
CAB and compression formats including
xz. See README for examples.
rextendr v0.2.0: Provides functions to compile and load Rust code from R along with helper functions to create R packages that use Rust code. There is a vignette on Using Rust code in R packages and another on Setting up Rust.
shinymeta v0.2.0.1: Provides tools for capturing logic in a Shiny app and exposing it as code that can be run outside of Shiny (e.g., from an R console). It also provides tools for bundling both the code and results to the end user. See README for examples
dynplot v1.1.1: Provides functions to visualize a single-cell trajectory as a graph or dendrogram as a dimensionality reduction or heatmap of the expression data, or a comparison between two trajectories as a pairwise scatterplot or dimensionality reduction projection. See Saelens et al. (2019) for background and the vignette for examples.
gridpattern 0.2.1: Provides grid grabs to fill in a user-defined plot area with various patterns, including geometric and image-based patterns, and support for custom user-defined patterns. There is a vignette.
netplot v0.1-1: Implements a graph visualization engine that puts an emphasis on aesthetics while providing default parameters that yield out-of-the-box-nice visualizations. There is a vignette with base plot examples, and another showing graph drawing with netplot.
NGLVieweR v1.3.1: Implements an htmlwidgets interface to NGL.js enabling users to visualize and interact with protein databank PDB and structural files in R and Shiny applications. See the vignette for examples.