November: “Top 40” New CRAN Packages
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Two hundred ninety-two new packages made it to CRAN in November. Picking forty was unusually difficult. Nevertheless, here are my “Top 40” selections in twelve categories: Archaeology, Computational Methods, Data, Epidemiology, Games, Machine Learning, Mathematics, Medicine, Statistics, Time Series, Utilities, and Visualization. R developers continue to extend the reach of R. November featured a new package on Archaeology, one of only seventeen I could find on CRAN pkgsearch::pkg_search(query="Archaeology ",size=200)
, as well as a package that wraps Python’s chess
package.
Looking back over the last twelve months my impression is that R continues to grow in the life sciences. Packages that I have classified as belonging to the categories Epidemiology, Genomics, or Medicine have comprised between ten and fourteen percent of the packages I have reviewed each month.
Archaeology
archeofrag v0.6.0: Implements methods based on graphs and graph theory for the stratigraphic analysis of fragmented objects in archaeology using “refitting” relationships between fragments scattered in stratigraphic layers. See the vignette.
Computational Methods
ADtools v0.5.4: Implements the forward-mode automatic differentiation for multivariate functions using the matrix-calculus notation from Magnus and Neudecker (2019). See the vignette for an introduction.
ML2Pvae v1.0.0: Provides functions to create a variational autoencoder (VAE) for parameter estimation in Item Response Theory (IRT) which allows straight-forward construction, training, and evaluation. Only minimal knowledge of tensorflow
or keras
is required. See Curi et al. (2019) for background and the vignette for an overview.
Data
campfin v1.0.4: Provides tools to explore and normalize American campaign finance data. This package was created by the Investigative Reporting Workshop to facilitate work on The Accountability Project. See the vignette to get started.
cpsvote v0.1.0: Provides automated methods for downloading, recoding, and merging selected years of the Current Population Survey’s Voting and Registration Supplement, a large national survey about registration, voting, and non-voting in United States federal elections. There are vignettes on basics, background, voting, and adding variables.
geogenr v1.0.0: Allows users to access geodatabasees and obtain information from the American Community Survey (ACS). See the vignette to get started.
openSkies v0.99.8: Provides a client interface to the OpenSky API that allows retrieval of flight information, as well as aircraft state vectors. See the vignette.
salem v0.2.0: Access data on all 152 accused witches from the 1692 Salem Witch Trials. There is an Introduction and a vignette reproducing an analysis.
Epidemiology
oxcgrt v0.1.0: Implements an interface to the Oxford COVID-19 Government Response Tracker (OxCGRT). There are vignettes on calculating incices and retrieving data.
PandemicLP v0.2.0: Implements the CovidL methodology for long-term epidemic and pandemic prediction. There is an Introduction and a case study.
SEIRfansy v1.1.0: Implements the Extended Susceptible-Exposed-Infected-Recovery Model for handling high false negative rate and symptom based administration of diagnostic tests. See Bhaduri et al. (2020) and the GitHub site for examples.
trendeval v0.0.1: Provides a coherent interface for evaluating models fit with the RECON trending
package. See README to get started.
Games
chess v1.0.1: Implements an “opinionated” wrapper around the python-chess
library allowing users to read and write PGN files as well as create and explore game trees such as the ones seen in chess books. See the vignettes chess, games, and advanced.
codebreaker v0.0.2: Inspired by Mastermind, the package implements a logic game in the style of the early 1980s home computers that can be played in the R console. Can you break the code? See README to start playing.
Machine Learning
fastai v2.0.2: Implements functions to simplify training neural networks based on best practices developed at fast.ai. See the website to get started and the twenty-three vignettes which include Audio Classification, Multilabel Classification and Medical Images.
mikropml v0.0.2: Implements the ML pipeline described in Topçuoğlu et al. (2020) For building machine learning models for classification and regression problems. There is an Introduction and an Overview.
stacks v0.1.0: Implements a grammar of model stacking for tidymodels
. There is a Getting Started Guide and a vignette on classification.
Mathematics
BaseSet v0.0.14: Implements a class and methods to work with sets, doing intersection, union, complementary sets, power sets, cartesian product and other set operations in a “tidy” way. See the Introduction, and the vignettes Advanced Examples and Fuzzy Sets.
viscomplexr v1.1.0: Provides functions to create phase portraits of functions in the complex number plane. See the vignette to get started.
Medicine
causalCmprisk v1.0.0: Provides functions to estimate average treatment effects of two static treatment regimes on time-to-event outcomes with competing events. The method uses propensity scores weighting for emulation of baseline randomization. See the vignette.
eventglm v1.0.2 Implements methods for doing event history regression for marginal estimands, including cumulative incidence the restricted mean survival, as described in the methodology reviewed in Andersen & Perme (2010). See the vignette for examples.
IPDfromKM v0.1.10: Implements a method to reconstruct individual patient data from Kaplan-Meier (KM) survival curves, visualize and assess the accuracy of the reconstruction, and perform secondary analysis on the reconstructed data. The package also implements iterative KM estimation algorithm proposed in Guyot (2012).
packDAMipd v0.1.2: Provides functions to construct both time-homogenous and time-dependent Markov models for cost-effectiveness analyses, perform decision analyses, and conduct deterministic and probabilistic sensitivity analyses. There are vignettes on deterministic and probabilistic sensitivity analyses, simple “sick-sicker” models, age-dependent “sick-sicker” models, and cycle dependent models.
reconstructKM v0.3.0: Provides functions for reconstructing individual-level data (time, status, arm) from Kaplan-MEIER curves published in academic journals. See Sun et al. (2018) for background and the vignette for the reconstruction procedure.
Statistics
ceser v1.0.0: Implements the Cluster Estimated Standard Errors method proposed in Jackson (2020) to compute clustered standard errors of linear coefficients in regression models with grouped data. See the vignette.
gfilmm v2.0.2: Implements generalized Fiducial inference for normal linear mixed models. Fiducial inference is similar to Bayesian inference in the sense that it represents the uncertainty about the parameters with a probability distribution. However, it does not require a prior. See Cisewski and Hannig (2012) for background and the vignette for examples.
hdpGLM v1.0.0: Implements MCMC algorithms to estimate the Hierarchical Dirichlet Process Generalized Linear Model presented in paper Ferrari (2020). See the vignette for examples.
latrend v1.0.1: Implements a framework for clustering longitudinal datasets in a standardized way. There is a Demo vignette and vignettes on implementing new models and validating cluster models.
mixComp v0.1-1: Implements methods to estimate the order of mixture distributions. See the vignette for an introduction to mixture models and and extended list of references.
monoClust v1.2.0: Implements the monothetic clustering algorithm for continuous data described in Chavent (1998). See the vignette.
potential v0.1.0: Implements the potential model for measuring social influences described in Stewart (1941). See the vignette for an introduction.
sftrack v0.5.2: Implements classes for tracking and movement data, building on sf
spatial infrastructure, and early theoretical work from Turchin (1998), and Calenge et al. (2009). There is an Overview along with the vignettes Reading in an sftrack, Structure, Fantastic Groups, and Getting Spatial.
simrec v1.0.0: Provides functions to simulate recurrent event data with a non-constant baseline hazard and possibly risk-free intervals and competing events. See Jahn-Eimermacher et al. (2015) for background and the vignette for an introduction.
Time Series
modeltime.resample v0.1.0: A modeltime
extension which implements forecast resampling tools to asses time-based model performance and stability for time series, panel data, and cross-sectional time series. There is a Getting Started guide and a vignette on Resampling.
tfarima v0.1.1: Provides tools to build customized transfer functions and ARIMA models with multiple operators and parameter restrictions. see Bell & Hilmer (1983) and Box & Tiao (1973) for background and the vignette for some theory and examples.
Utilities
getDTeval v0.0.1: Provides functions to translate statements that use get()
or eval()
to improve run-time efficiency. See the vignette.
lineup2 v0.2-5: Provides tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. There is a vignette.
sdcLog v0.1.0: Tools for researchers to explicitly show that their results comply to rules for statistical disclosure control imposed by research data centers. The methods used are described in Bond et al. (2015). There is an Introduction and a vignette on options.
Visualization
leaflet.multiopacity v0.1.1: Extends leaflet
by adding a widget to control the opacity of multiple layers. There are vignettes for using the package with leaflet and leafletProxy.
mapboxer v0.4.0: Provides access to Mapbox GL JS, an open source JavaScript library that uses WebGL to render interactive maps via the htmlwidgets
package. Visualizations can be used from the R console, in R Markdown documents and in Shiny apps. See the vignette to get started.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.