One hundred and fifty-one new packages arrived at CRAN in February. Here are my “Top 40” picks organized into eight categories: Bioinformatics, Data, Machine Learning, Medicine, Statistics, Time Series, Utilities and Visualization.
Cascade v1.7: Implements a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. See Jung et al. (2014) for details, along with a Package Introduction and a vignette on re-analysis.
noaaoceans v0.1.0: Provides tools to access the National Oceanic and Atmospheric Administration (NOAA) API. See the vignette for details.
RobinHood v:1.0.1: Implements an interface to the RobinHood investing platform, including the ability to access account data, retrieve investment statistics and quotes, place and cancel orders, and more.
stlcsb v0.1.2: Provides functions working with data from The Citizens’ Service Bureau of the City of St. Louis including downloading data, categorizing problem requests, cleaning and subsetting CSB data, and projecting the data using the x and y coordinates. See the vignette.
bigMap v2.1.0: Implements an unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. See Garriga and Bartumeus (2018) and the vignette for details.
gama v1.0.3: Implements a genetic, evolutionary approach to performing hard partitional clustering. For details see Scrucca (2013), Charrad et al. (2014), and Tsagris and Papadakis (2018). The vignette shows how to use the package.
leiden v0.2.3: Uses
reticulate to implement the
Python leidenalg clustering algorithm for partitioning graphs in to communities in R. See the
Python repository and Traag et al (2018) for details. There is also a vignette.
r.blip v1.1: Provides functions to learn Bayesian networks from datasets containing thousands of variables, and includes algorithms for (1) parent set identification (Scanagatta (2015)), (2) general structure optimization (Scanagatta (2018)), (3) bounded tree width structure optimization (Scanagatta (2016)), and (4) structure learning on incomplete data sets (Scanagatta (2018)).
RTML v0.9: Implements efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. The details are described Cao and Schwarz (2018). There is a Tutorial.
SAR v1.0.0: Provides both a stand-alone and Azure Cloud implementation of the Smart Adaptive Recommendations (SAR) algorithm for personalized recommendations. Look here for a description of the SAR algorithm.
ClinReport v0.9.1.11: Provides functions to create formatted statistical tables in Microsoft Word documents that meet clinical standards. There is a vignette for Getting Started, a vignette for Modifying Outputs, and another for Graphic Outputs.
dosearch v1.0.2: Implements a method to identify causal effects from arbitrary observational and experimental probability distributions via do-calculus and standard probability manipulations, using a search-based algorithm that handles selection bias (Bareinboim and Tian (2015)), transportability (Bareinboim and Pearl (2014)), missing data (Mohan et al. (2013)), and arbitrary combinations of these. There is an informative Introduction
interactions v1.0.0: Provides a suite of functions for conducting and interpreting the analysis of statistical interaction in regression models, and includes visualization of two- and three-way interactions. There is a vignette for Exploring Interactions and another for Plotting Interactions.
IrregLong v0.1.1: Provides functions to analyze longitudinal data for which the times of observation are random variables that are potentially associated with the outcome process, and includes inverse-intensity weighting methods (Lin et al. (2004)) and multiple outputation (Pullenayegum (2016)). Look here for an overview.
OutlierDetection v0.1.0: Implements various methods to detect outliers including: model-based (Barnett (1978)), distance-based (Hautamaki et al. (2004)), dispersion-based (Jin et al. (2001)), depth-based (Johnson et al. (1998)), and density-based (Ester et al. (1996)).
PointFore v0.2.0: Provides functions to estimate specification models for the state-dependent level of an optimal quantile/expectile forecast along with Wald Tests and a test of overidentifying restrictions. There is a Tutorial and vignettes on the GDP Greenbook and Preciptation examples.
segmenTier v0.1.2: Implements a dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments, based on the theory described in Machne et al. (2017). The vignette provides an Introduction.
Rlgt v0.1-2: Provides functions to use
rstan to fit several Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. There is an Intorduction to global trend time series forecasting and an Introduction to the package.
tsfeatures v1.0.0: Implements methods for extracting various features from time series data as described in Hyndman et al. (2013) , Kang et al.(2017) and Fulcher et al. (2013). The vignette contains examples.
rosr v0.0.5: Provides methods to create reproducible academic projects with integrated academic elements, including datasets, references, codes, images, manuscripts, dissertations, slides and so on.
jskm v0.3.1: Provides the function
jskm() to create publication quality Kaplan-Meier plots with at-risk tables below, and
svyjskm() to plot a weighted Kaplan-Meier estimator.