Of the 182 new packages that made it to CRAN in October, here are my picks for the “Top 40”. They are organized into eight categories: Engineering, Machine Learning, Numerical Methods, Science, Statistics, Time Series, Utilities and Visualizations. Engineering is a new category, and its appearance may be an early signal for the expansion of R into a new domain. The Science category is well-represented this month. I think this is the result of the continuing trend for working scientists to wrap their specialized analyses into R packages.
rroad v0.0.4: Computes and visualizes the International Roughness Index (IRI) given a longitudinal road profile for a single road segment, or for a sequence of segments with a fixed length. For details on The International Road Roughness Experiment establishing a correlation and a calibration standard for measurements, see the World Bank technical paper. The vignette shows an example of a Road Condition Analysis. The following
scaleogram was produced from a continuous wavelet transform of a 3D accelerometer signal.
MlBayesOpt v0.3.3: Provides a framework for using Bayesian optimization (see Shahriari et al. to tune hyperparameters for support vector machine, random forest, and extreme gradient boosting models. The vignette shows how to set things up.
rerf v1.0: Implements an algorithm, Random Forester (RerF), developed by Tomita (2016), which is similar to the Random Combination (Forest-RC) algorithm developed by Breiman (2001). Both algorithms form splits using linear combinations of coordinates.
KGode v1.0.1: Implements the kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016), and the warping algorithm proposed in Niu et al. (2017) for improving parameter estimation in ODEs.
adjclust v0.5.2: Implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated with a position, and only adjacent clusters can be merged. The algorithm, which is time- and memory-efficient, is described in Alia Dehman (2015). There are vignettes on Clustering Hi-C Contact Maps, Implementation Notes, and Inferring Linkage Disequilibrium blocks from Genotypes.
hsdar v0.6.0: Provides functions for transforming reflectance spectra, calculating vegetation indices and red edge parameters, and spectral resampling for hyperspectral remote sensing and simulation. The Introduction offers several examples.
mapfuser v0.1.2: Constructs consensus genetic maps with LPmerge (See Endelman and Plomion (2014)) and models the relationship between physical distance and genetic distance using thin-plate regression splines (see Wood (2003)). The vignette explains how to use the package.
BayesRS v0.1.2: Fits hierarchical linear Bayesian models, samples from the posterior distributions of model parameters in JAGS, and computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio ([See Wetzels et al.(2009). There is an Introduction.
CatPredi v1.1: Allows users to categorize a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximizing the discriminative ability of the model. See Barrio et al. (2015) and Barrio et al. (2017).
CovTools v0.2.1: Provides a collection of geometric and inferential tools for convenient analysis of covariance structures. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987).
emmeans v0.9.1: Provides functions to obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models, and computes contrasts or linear functions of EMMs, trends, and comparisons of slopes. There are twelve vignettes including The Basics, Comparisons and Contrasts, Confidence Intervals and Tests, Interaction Analysis, and Working with Messy Data.
ESTER v0.1.0: Provides an implementation of sequential testing that uses evidence ratios computed from the Akaike weights of a set of models. For details see Burnham & Anderson (2004). There is a vignette.
FarmTest v1.0.0: Provides functions to perform robust multiple testing for means in the presence of latent factors. It uses Huber’s loss function to estimate distribution parameters and accounts for strong dependence among coordinates via an approximate factor model. See Zhou et al.(2017) for details. There is a vignette to get you started.
miic v0.1: Implements an information-theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many datasets. For more information see Verny et al. (2017).
modcmfitr v0.1.0: Fits a modified version of the Connor-Mosimann distribution ( Connor & Mosimann (1969)), a Connor-Mosimann distribution, or a Dirichlet distribution to elicited quantiles of a multinomial distribution. See the vignette for details.
rENA v0.1.0: Implements functions to perform epistemic network analysis ENA, a novel method for identifying and quantifying connections among elements in coded data, and representing them in dynamic network models, which illustrate the structure of connections and measure the strength of association among elements in a network.
carfima v1.0.1: Provides a toolbox to fit a continuous-time, fractionally integrated ARMA process CARFIMA on univariate and irregularly spaced time-series data using a general-order CARFIMA(p, H, q) model for p>q as specified in Tsai and Chan (2005).
colorednoise v0.0.1: Provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation) based on work by Ruokolainen et al. (2009). The vignette describes colored noise.
oshka v0.1.2: Expands quoted language by recursively replacing any symbol that points to quoted language with the language itself. There is an Introduction and a vignette on Non Standard Evaluation Functions.
usethis v1.1.0: Automates package and project setup tasks, including setting up unit testing, test coverage, continuous integration, Git, GitHub, licenses,
Rcpp, RStudio projects, and more that would otherwise be performed manually. README provides examples.
xltabr v0.1.1: Provides functions to produce nicely formatted cross tabulations to Excel using [openxlsx]((https://cran.r-project.org/package=openxlsx), which has been developed to help automate the process of publishing Official Statistics. Look here for documentation.
otvPlots v0.2.0: Provides functions to automate the visualization of variable distributions over time, and compute time-aggregated summary statistics for large datasets. See the README for an introduction.