{"id":342645,"date":"2023-05-24T18:00:00","date_gmt":"2023-05-25T00:00:00","guid":{"rendered":"https:\/\/rviews.rstudio.com\/2023\/05\/25\/april-2023-top-40-new-cran-packages\/"},"modified":"2023-05-24T18:00:00","modified_gmt":"2023-05-25T00:00:00","slug":"april-2023-top-40-new-cran-packages","status":"publish","type":"post","link":"https:\/\/www.r-bloggers.com\/2023\/05\/april-2023-top-40-new-cran-packages\/","title":{"rendered":"April 2023: “Top 40” New CRAN Packages"},"content":{"rendered":"\r\n\r\n
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One hundred fifty-six new packages made it to CRAN in April. Here are my \u201cTop 40\u201d selections in twelve categories: Computational Methods, Data, Ecology, Economics, Genomics, Machine Learning, Mathematics, Medicine, Science, Statistics, Utilities, and Visualization.<\/p>\n\n

Computational Methods<\/h3>\n\n

clarabel<\/a> v0.4.1: Implements Clarabel<\/a>, a versatile interior point solver that solves linear programs, quadratic programs, second-order cone programs, and problems with exponential and power cone constraints. See the vignette<\/a>.<\/p>\n\n

condor<\/a> v1.0.0: Provides functions to access the Condor<\/a> high performance computing environment. Files are first uploaded to a submitter machine and the resulting job is then passed on to Condor. Look here<\/a> for the code.<\/p>\n\n

GPUmatrix<\/a> v0.1.0: Extends R to use GPUs for matrix computations. See the vignette<\/a>.<\/p>\n\n

\"Plots<\/p>\n\n

hydroMOPSO<\/a> v0.1-3: Implements a state-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO)<\/a>, based on the algorithm developed by Lin et al. (2018)<\/a> with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020)<\/a> which can be used for global optimization of non-smooth and non-linear R functions and other models that need to be run from the system console, e.g. SWAT+<\/a>.<\/p>\n\n

Data<\/h3>\n\n

dataverifyr<\/a> v0.1.5: Provides a thin wrapper around dplyr<\/code>, data.table<\/code>, arrow<\/code>, and DBI<\/code> to allow users to define rules which can be used to verify a given dataset. See README<\/a> to get started.<\/p>\n\n

\"Plot<\/p>\n\n

neotoma2<\/a> v1.0.0: Provides functions to access and manipulate data in the Neotoma Paleoecology Database<\/a>. See the vignette<\/a>.<\/p>\n\n

\"Diagram<\/p>\n\n

rpaleoclim<\/a> v1.0.0: Implements an interface to PaleoClim<\/a>, a set of free, high resolution paleoclimate surfaces covering the whole globe that includes data on surface temperature, precipitation and the standard bioclimatic variables commonly used in ecological modelling. See Brown et al. (2019)<\/a> for background and the vignette<\/a>.<\/p>\n\n

zctaCrosswalk<\/a> v2.0.0: Contains the US Census Bureau\u2019s 2020 ZCTA to County Relationship File, as well as convenience functions to translate between States, Counties and ZIP Code Tabulation Areas (ZCTAs). See the Introduction<\/a> and the vignettes Workflow with tidycensus<\/a>, and Developer Notes<\/a>.<\/p>\n\n

Ecology<\/h3>\n\n

EWSmethods<\/a> v1.1.2: Implements methods for forecasting tipping points at the community level that include rolling and expanding window approaches to assessing abundance based early warning signals, non-equilibrium resilience measures, and machine learning. See Dakos et al. (2012)<\/a>, Deb et al. (2022)<\/a>, and Drake and Griffen (2010)<\/a> for background and the vignette<\/a> for an introduction.<\/p>\n\n

\"Plots<\/p>\n\n

fqacalc<\/a> v1.0.0: Provides functions for calculating Floristic Quality Assessment (FQA) metrics using regional FQA databases that have been approved or approved with reservations as ecological planning models by the U.S. Army Corps of Engineers (USACE). For information on FQA see Spyreas (2019)<\/a>. There is an Introduction<\/a>.<\/p>\n\n

Economics<\/h3>\n\n

clptheory<\/a> v0.1.0: Provides functions to compute the uniform rate of profit, the vector of price of production and the vector of labor values, and also compute measures of deviation between relative prices of production and relative values. See Basu and Moraltis (2023)<\/a> for background and README<\/a> for an introduction.<\/p>\n\n

Genomics<\/h3>\n\n

BREADR<\/a> v1.0.1: Implements a method for estimating degrees of relatedness for extreme low-coverage genotype data and includes functions to quantify and visualize the level of confidence in the estimated degrees of relatedness. See Rohrlach et al. (2023)<\/a> for package details and README<\/a> for examples.<\/p>\n\n

\"Plots<\/p>\n\n

crosshap<\/a> v1.2.2: Implements a local haplotyping visualization toolbox to capture major patterns of co-inheritance between clusters of linked variants, while connecting findings to phenotypic and demographic traits across individuals. See Marsh et al. (2022)<\/a> for a detailed example and README<\/a> for an introduction.<\/p>\n\n

\"Visualization<\/p>\n\n

DAISIEprep<\/a> v0.3.2: Extracts colonization and branching times of island species for analysis with the DAISIE<\/code> package. There is a Tutorial<\/a> and there are vignettes on Performance<\/a> and Sensitivity<\/a>.<\/p>\n\n

\"Endemicity<\/p>\n\n

Machine Learning<\/h3>\n\n

CCMMR<\/a> v0.1: Implements the convex clustering through majorization-minimization algorithm described in Touw, Groenen, and Terada (2022)<\/a> to minimize the convex clustering loss function. See README<\/a> for examples.<\/p>\n\n

rcccd<\/a> v0.3.2: Provides functions to fit class cover catch digraph classification models. Methods are explained in Priebe et al. (2001)<\/a>, Priebe et al. (2003)<\/a>, and Manukyan and Ceyhan (2016)<\/a>. README<\/a> contains some description.<\/p>\n\n

TheOpenAir<\/a> v0.1.0: Implements a wrapper using the OpenAI API<\/a> as a back end to integrate ChatGPT<\/code>into diverse data-related tasks, such as data cleansing and automating analytics scripts. See README<\/a> to get started.<\/p>\n\n

Mathematics<\/h3>\n\n

cyclotomic<\/a> v1.1.0: Implements algorithms from the GAP project<\/a> to work with cyclotomic numbers: complex numbers that can be thought of as the rational numbers extended with the roots of unity. They have applications in number theory, algebraic geometry, algebraic number theory, coding theory, in the theory of graphs and combinatorics, and in the theory of modular functions and modular curves. See README<\/a> for examples.<\/p>\n\n

markovmix<\/a> v0.1.1: Provides functions to fit a mixture of Markov chains of higher orders from multiple sequences along with various utility functions to derive transition patterns, transition probabilities per component and component priors. See README<\/a> for examples.<\/p>\n\n

Medicine<\/h3>\n\n

DiDforBigData<\/a> v1.0: Provides a big-data-friendly and memory-efficient difference-in-differences estimator for staggered (and non-staggered) treatment contexts. See the Get Started<\/a> Guide the vignettes Background<\/a>, Examples<\/a>, and Theory<\/a>.<\/p>\n\n

\"Run<\/p>\n\n

predictNNB<\/a> v0.1.0: Provides tools to estimate when and where a model-guided treatment strategy may outperform a treat-all or treat-none approach using Monte Carlo simulation and evaluation of the Net Monetary Benefit. See Parsons et al. (2023)<\/a> for details, the Introduction<\/a>, and the vignettes on creating functions<\/a>, summarising results<\/a>, and detailed example<\/a>.<\/p>\n\n

\"Plot<\/p>\n\n

predRupdate<\/a> v0.1.0: Provides functions to evaluate the predictive performance of existing clinical prediction model given a new dataset. See Su et al. (2018)<\/a>, Debray et al. (2014)<\/a>, and Martin et al. (2018)<\/a> for background and the vignettes Introduction<\/a> and Technical Background<\/a>.<\/p>\n\n

SPARRAfairness<\/a> v0.0.0.1: Provides functions to analyse the behavior and performance of the Scottish Patients At Risk of admission and Re-Admission risk score which estimates yearly risk of emergency hospital admission using electronic health records for most of the Scottish population. Analysis focuses on differential performance over demographically-defined groups. See the vignette<\/a>.<\/p>\n\n

\"Plot<\/p>\n\n

Science<\/h3>\n\n

kronos<\/a> v1.0.0: Implements a framework to analyse circadian or otherwise rhythmic data using the familiar R linear modelling syntax, while taking care of the trigonometry under the hood. Look here<\/a> for examples.<\/p>\n\n

\"Plot<\/p>\n\n

mpmsim<\/a> v1.0.0: Provides functions to to simulate matrix population models with particular characteristics based on aspects of life history such as mortality trajectories and fertility trajectories, and allows the exploration of sampling error due to small sample size. See the vignettes on robustness<\/a>, sampling error & propagation<\/a>, and PCA<\/a>.<\/p>\n\n

\"Plot<\/p>\n\n

Statistics<\/h3>\n\n

BGFD<\/a> v0.1: Implements the probability density function, cumulative distribution function, quantile function, random numbers, survival function, hazard rate function, and maximum likelihood estimates for the family of Bell-G and Complementary Bell-G distributions. See\nFayomi et al. (2022)<\/a>, Alanzi et al.(2023)<\/a>, and Algarni (2022)<\/a> for details.<\/p>\n\n

D3mirt<\/a> v1.0.3: Provides functions for identifying, estimating, and plotting descriptive multidimensional item response theory models, restricted to 3D and dichotomous or polytomous data that fit the two-parameter logistic model or the graded response model. See\nthe vignette<\/a> for an extensive introduction.<\/p>\n\n

\"Data<\/p>\n\n

funStatTest<\/a> v1.0.2: Implements two sample comparison procedures based on median-based statistical tests for functional data, described in Smida et al. (2022)<\/a>, Chakraborty and Chaudhuri (2015)<\/a>, Horvath et al. (2013<\/a>, and Cuevas et al. (2004)<\/a>. See the vignette<\/a> for examples.<\/p>\n\n

lessSEM<\/a> v1.4.16: Provides regularized structural equation modeling (regularized SEM) with non-smooth penalty functions (e.g., lasso) building on lavaan<\/code>. There are nine vignettes including: lessSEM<\/a>, The Structural Equation Model<\/a>, and Mixed Penalties<\/a>.<\/p>\n\n

\"Plot<\/p>\n\n

panelhetero<\/a> v1.0.0: Provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis using the methods developed by Okui and Yanagi (2019)<\/a> and Okui and Yanagi (2020)<\/a>. See the vignette<\/a>.<\/p>\n\n

tdsa<\/a> v1.0-1: Provides functions to perform time-dependent sensitivity analysis by calculating time-dependent state and parameter sensitivities for both continuous- and discrete-time deterministic models. See Ng et al. (in review)<\/a> for background and the vignette<\/a> to get started.<\/p>\n\n

\"Plot<\/p>\n\n

Utilities<\/h3>\n\n

crew.cluster<\/a> v0.1.0: Extends the mirai<\/code>-powered crew<\/code> package with worker launcher plugins for traditional high-performance computing systems to enable statisticians and data scientists to asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. Look here<\/a> to get started.<\/p>\n\n

duke<\/a> v0.0.1: Provides functions to generate visualizations with Duke\u2019s official suite of colors in a color blind friendly way. There is an Overview<\/a> and four additional vignettes including one on the theme_duke()<\/a> function.<\/p>\n\n

\"Plot<\/p>\n\n

grateful<\/a> v0.2.0: Facilitates the citation of R packages used in analysis projects by providing functions to scan projects for packages used and produces documents with citations in the preferred bibliography format. Functions may be used within rarkdown<\/code>or quarto<\/code> documents. See README<\/a> for examples.<\/p>\n\n

hightR<\/a> v0.3.0: Implements the HIGHT<\/a> block cipher encryption algorithm developed to provide confidentiality in low power consumption computing environments such Radio-Frequency Identification and Ubiquitous Sensor Network. Look here<\/a> for more information.<\/p>\n\n

myCRAN<\/a> v1.0: Provides functions to plot the daily and cumulative number of downloads of R<\/code> packages, obtaining daily and cumulative counts in one run. See the vignette<\/a>.<\/p>\n\n

\"Plot<\/p>\n\n

woodendesc<\/a> v0.1.0: Provides functions to simplify obtaining available packages, their version codes and dependencies from any R<\/code> repository. Uses extensive caching for repeated queries. See README<\/a>for examples.<\/p>\n\n

Visualization<\/h3>\n\n

fxl<\/a> v1.6.3: Provides functions to prepare and design single case design<\/a> figures that are typically prepared in spreadsheet software. See the vignette<\/a> for theory and examples.<\/p>\n\n

\"Plot<\/p>\n\n

ggragged<\/a> v0.1.0: Extends ggplot2<\/code> facets to panel layouts arranged in a grid with ragged edges with rows and columns of potentially varying lengths. These may be useful in representing nested or partially crossed relationships between faceting variables. See README<\/a> for examples.<\/p>\n\n

\"Grid<\/p>\n\n

nndiagram<\/a> v1.0.0: Generates LaTeX<\/code> code for drawing well-formatted neural network diagrams with TikZ<\/code><\/a>. Users define the number of neurons on each layer, neuron connections to keep or omit, layers considered to be oversized, and neurons to draw with lighter color. See README<\/a> for instructions.<\/p>\n\n

\"Neural<\/p>\n\n

PlotTools<\/a> v0.2.0: Provides functions to manipulate irregular polygons and annotate plots with legends for continuous variables and color spectra using the base graphics plotting tools. See README<\/a> for an example.<\/p>\n\n

\"Scatter<\/p>\n\n