Co-Data Learning for Bayesian Additive Regression Trees

Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the 'dbarts' 'R' package. See Goedhart and others (2023) for details.

ProPublica API Client

Client for accessing data journalism APIs from ProPublica < http://www.propublica.org/>.

Fused Partitioned Regression for Clinical and Omics Data

Fit (generalized) linear regression models in each leaf node of a tree. The tree is constructed using clinical variables only. The linear regression models are constructed using (high-dimensional) omics variables only. The leaf-node-specific regression models are estimated using the penalized likelihood including a standard ridge (L2) penalty and a fusion penalty that links the leaf-node-specific regression models to one another. The intercepts of the leaf nodes reflect the effects of the clinical variables and are left unpenalized. The tree, fitted with the clinical variables only, should be constructed outside of the package with the 'rpart' 'R' package. See Goedhart and others (2024) for details on the method.

Provide the 'x13ashtml' Seasonal Adjustment Binary

The US Census Bureau provides a seasonal adjustment program now called 'X-13ARIMA-SEATS' building on both earlier programs called X-11 and X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau offers both source and binary versions -- which this package integrates for use by other R packages.

Another Approach to Package Installation

The goal of 'pak' is to make package installation faster and more reliable. In particular, it performs all HTTP operations in parallel, so metadata resolution and package downloads are fast. Metadata and package files are cached on the local disk as well. 'pak' has a dependency solver, so it finds version conflicts before performing the installation. This version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well.

A 'HTTP' Server Graphics Device

A graphics device for R that is accessible via network protocols. This package was created to make it easier to embed live R graphics in integrated development environments and other applications. The included 'HTML/JavaScript' client (plot viewer) aims to provide a better overall user experience when dealing with R graphics. The device asynchronously serves graphics via 'HTTP' and 'WebSockets'.

Classes for 'GeoJSON'

Classes for 'GeoJSON' to make working with 'GeoJSON' easier. Includes S3 classes for 'GeoJSON' classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited 'GeoJSON'; and serializing to/from 'Geobuf' binary 'GeoJSON' format.

'NoSQL' Database Connector

Simplified JSON document database access and manipulation, providing a common API across supported 'NoSQL' databases 'Elasticsearch', 'CouchDB', 'MongoDB' as well as 'SQLite/JSON1', 'PostgreSQL', and 'DuckDB'.

RDF Library Bindings in R

Provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). RDF is described at < https://www.w3.org/TR/rdf-primer/>. This package supports RDF by implementing an R interface to the Redland RDF C library, described at < https://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes.

Modelling of Population Growth

Modelling of population growth under static and dynamic environmental conditions. Includes functions for model fitting and making prediction under isothermal and dynamic conditions. The methods (algorithms & models) are based on predictive microbiology (See Perez-Rodriguez and Valero (2012, ISBN:978-1-4614-5519-6)).