May 2022: “Top 40” New CRAN Packages
This article is originally published at https://rviews.rstudio.com/
One hundred seventy-nine new packages made it to CRAN in May. Here are my “Top 40” picks in twelve categories: Computational Methods, Data, Ecology, Epidemiology, Finance, Machine Learning, Networks, Science, Statistics, Time Series, Utilities, and Visualization.
graDiEnt v1.0.1: Implements the derivative-free, optim-style Stochastic Quasi-Gradient Differential Evolution optimization algorithm published in Sala, Baldanzini, and Pierini (2018) that uses population members to build stochastic gradient estimates. See README for an example.
rxode2 V2.0.7: Provides facilities for running simulations from ordinary differential equation models, such as pharmacometrics and other compartmental models, but requires both C and Fortran compilers. See the vignette for an example.
ScaleSpikeSlab v1.0: Provides a scalable Gibbs sampling implementation for high dimensional Bayesian regression with the continuous spike-and-slab prior described in Biswas, Mackey & Meng (2022). See README for an example.
bluebike v0.0.3: Provides functions that facilitate importing and working with the Boston Blue Bike Data Set including functions to compute trip distances and map the locations of stations within a given radius. See the vignette for examples.
eurodata v1.4.2: Implements an interface to Eurostat’s Bulk Download Facility with fast
data.table based import of data, labels, and metadata along with data search and data description and comparison functions. See README to get started.
gbifdh v0.1.2: Implements a high performance interface to the Global Biodiversity Information Facility that supports large-scale analyses using
dplyr operations on complete tables. See the vignette for examples.
FIESTA v3.4.1: Implements an estimation tool for analysts that work with sample-based inventory data from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis Program. There are nine vignettes including manuals for Module Estimates and Population Data, and Small Area Estimators and Spatial Tools.
soiltestcorr v2.1.2: Provides functions designed to assist users on the correlation analysis of crop yield and soil test values including functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches. See Correndo et al. (2017), Cate & Nelson (1971), Anderson & Nelson (1975), Bullock & Bullock (1994) and Melsted & Peck (1977) for background. There are seven vignettes including an Introduction and Quadratic-plateau response.
sspm v0.9.1: Implement a gam-based spatial surplus production model, aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. There is a vignette on Package and Workflow design and another that provides an example with simulated data.
EpiInvert v0.1.1: Inverts a renewal equation to estimate time-varying reproduction numbers and restored incidence curves with festive days and weekly biases corrected as described in Alvarez et al. (2021) and Alvarez et al. (2022). See README for examples.
linelist v0.0.1: Provides tools to help storing and handling case line list data by adding a tagging system to classical
data.frame objects to identify key epidemiological data. See the vignette for examples.
markets v1.0.3: Provides functions to estimate markets in equilibrium and disequilibrium based on full information maximum likelihood techniques given in Maddala and Nelson (1974) and implemented using the analytic derivative expressions calculated in Karapanagiotis (2020). There is an Overview providing theory and code and vignettes on Model initializion details, Market-clearing assessment, and Use cases.
portvine v1.01: Provides portfolio level risk estimates including value at Risk and Expected Shortfall following the approach described in Sommer (2022) by modeling each asset with an ARMA-GARCH model and then modeling their cross dependency via a Vine Copula in a rolling window fashion. See the vignette to get started.
usincometaxes v0.4.0: Implements a wrapper to the NBER’s TAXSIM 35 tax simulator. TAXSIM 35 to calculate federal and state income taxes. There are vignettes on Uploading Data, Input Columns, Output Columns, and Calculating Taxes.
MixviR v3.3.5: Implements tools for exploring DNA and amino acid variation and inferring the presence of target lineages from microbial high-throughput genomic DNA samples that potentially contain mixtures of variants/lineages. MixviR was originally created to help analyze environmental SARS-CoV-2/Covid-19 samples from environmental sources such as waste water or dust, but can be applied to any microbial group. See DePristo et al. (2011) and Danecek et al. (2021) for background, and the vignette for examples.
simer v0.9.0.0: Implements a data simulator including genotype, phenotype, pedigree, selection and reproduction for animals and plants and provides data for genomic gelection, genome-wide association, and breeding studies. See Kao and Zeng (2002) and Ripley (1987) for background. Look here for extensive documentation.
fastTopics v0.6-135: Implements fast, scalable optimization algorithms for fitting topic models and non-negative matrix factorization for count data. The methods exploit the special relationship between the multinomial topic model (probabilistic latent semantic indexing) and Poisson non-negative matrix factorization. See the vignettes: Relationship between NMF and topic modeling and Topic mideling vs. clustering.
metrica v1.2.3: Provides functions to evaluate prediction performance of point-forecast models accounting for different aspects of the agreement between predicted and observed values including error metrics, model efficiencies, indices of agreement, goodness of fit, concordance correlation, and error decomposition, and plots the visualized agreement. See the vignettes on Available Metrics and Model Assessment.
EvoPhylo v0.1: Provides functions to support automated morphological character partitioning for phylogenetic analyses, and analyses of macroevolutionary parameter outputs. See Simões and Pierce (2021) for background. There is a vignette on Theoretical Background, and there are others on Character Partitioning, FBD parameters, and Evolutionary Rates.
sits v1.0.0: Provides an end-to-end toolkit for land use and land cover classification using big Earth observation data, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021). Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, and Digital Earth Africa using the STAC protocol and the
gdalcubes package. An eBook provides extensive documentation.
biosensors.usc v1.0: Provides a framework for using distributional representations of biosensor data such as ECG, medical imaging or fMRI data in various statistical modeling tasks: regression models, hypothesis testing, cluster analysis, visualization, and descriptive analysis. See Matabuena et al. (2021) for background and the vignette for an introduction.
GeoModels v1.0.1: Provides functions to analyze Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data and simulate random fields using likelihood methods. See Bevilacqua and Gaetan (2015) and Vallejos et al. (2020) for background, and look here for examples.
nlmixr2 v2.0.6: Fit and compare nonlinear mixed-effects models with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics using differential equations solved by compiled C code provided in the
rxode2 package. See Almquist et al. (2015) and Wang et al. (2015) for background, and the vignette for examples.
stdmod v0..1.7.1: Provides functions for computing a standardized moderation effect in moderated regression and forming its confidence interval by nonparametric bootstrapping as proposed in Cheung et al. (2002). There are six vignettes including a Quick Start Guide and a vignette on Standardized Moderation.
forceR v1.0.15: Initially written and optimized to deal with insect bite force measurements, the functions in this package can be used to clean and analyze any time series. They provide a workflow to load, plot and crop data, correct amplifier and baseline drifts, identify individual peak shapes, rescale (normalize) peak curves, and find best polynomial fits to describe and analyze force curve shapes. See the vignette for examples.
ZINARp v0.1.0: Provides functions for simulation, exploratory data analysis and Bayesian analysis of p-order integer-valued autoregressive, INAR(p), and zero-inflated p-order integer-valued autoregressive, ZINAR(p), processes, as described in Garay et al. (2020).
async v0.2.1: Provides functions for writing sequential-looking code that pauses and resumes similarly to generator and async constructs from
promises packages. See the vignette for an example.
chronicler v0.2.0: Provides tools to decorate a function so that it returns its along with a log detailing when the function was run, what were its inputs, what were the errors (if the function failed to run) and other useful information. See the vignettes: A non-mathematician’s introduction to monads, The Maybe monad, and A real world example.
partialised v0.1.0: Provides a partialised class that extends the partialising function of
purrr by making it easier to change the arguments. This is similar to the function-like object in `Julia’. See README for an example.
shinybrowser v1.0.0: Provides information about
shiny app users including browser name and version, device type (mobile or desktop), operating system and version, and browser dimensions. See README for more information.
ggbraid v0.2.2: Implements
stat_braid(), that extends the functionality of
geom_ribbon() to correctly fill the area between two alternating lines (or steps) with two different colors, and
geom_braid(). Three vignettes, US Supreme Court, NBA Finals Game, and Average Daily Temperatures provide examples.
UpSetVP v1.0.0: Uses the ideas of variance partitioning and hierarchical partitioning as described in Lai et al. (2022) to visualize the unique, common, or individual contribution of each predictor (or matrix of predictors) towards explaining variation. Look here for examples.
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