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.

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.

Functions to append confidence intervals, prediction intervals,
    and other quantities of interest to data frames. All appended quantities
    are for the response variable, after conditioning on the model and covariates.
    This package has a data frame first syntax that allows for easy piping.
    Currently supported models include (log-) linear, (log-) linear mixed,
    generalized linear models, generalized linear mixed models, and
    accelerated failure time models.