Client for the Open Citations Corpus (<http://opencitations.net/>).
    Includes a set of functions for getting one identifier type from another,
    as well as getting references and citations for a given identifier.

Deduplicates bibliographic citations from multiple sources while
             preserving customizable metadata, supporting data-driven search
             strategy development and evidence synthesis reporting. Search
             results can be analyzed using plots and tables, and imported or
             exported in 'RIS' and 'CSV' formats. An interactive 'shiny'
             application is included for exploratory use.

Tests whether missingness in explanatory variables is
    conditionally independent of the outcome, given observed data. Uses
    multiply-imputed datasets and cross-validated classifiers to produce a
    test statistic and p-value, with a sensitivity parameter (kappa) for
    calibrating interpretation. Wraps the 'citest' 'Python' engine via a
    local 'FastAPI' server over 'HTTP', so no 'reticulate' dependency is
    needed at runtime.

Simulates clinical trials and summarizes causal effects and treatment policy estimands in the presence of intercurrent events in a transparent and intuitive manner.

A systematic biology tool was developed to identify cell infiltration via Individualized Cell-Cell interaction network. 'CITMIC' first constructed a weighted cell interaction network through integrating Cell-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific sample, and then, it used a network propagation algorithm on the network to identify cell infiltration for the sample. Ultimately, cell infiltration in the patient dataset was obtained by normalizing the centrality scores of the cells.

The 'cito' package provides a user-friendly interface for training and interpreting deep neural networks (DNN). 'cito' simplifies the fitting of DNNs by supporting the familiar formula syntax, hyperparameter tuning under cross-validation, and helps to detect and handle convergence problems.  DNNs can be trained on CPU, GPU and MacOS GPUs. In addition, 'cito' has many downstream functionalities such as various explainable AI (xAI) metrics (e.g. variable importance, partial dependence plots, accumulated local effect plots, and effect estimates) to interpret trained DNNs. 'cito' optionally provides confidence intervals (and p-values) for all xAI metrics and predictions. At the same time, 'cito' is computationally efficient because it is based on the deep learning framework 'torch'. The 'torch' package is native to R, so no Python installation or other API is required for this package.