Multiple Imputation for Informative Censoring. This package implements two methods. Gamma Imputation described in <DOI:10.1002/sim.6274> and Risk Score Imputation described in <DOI:10.1002/sim.3480>.

Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Here, we presented Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a novel network-based model that integrates multiple data types to predict immunotherapy outcomes. It incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune-related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. By identifying key genetic and immunological factors, it provides an insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms.

This package provides functions for computing the global and local Gaussian density estimates based on the ICV bandwidth. See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 <doi:10.1198/jasa.2010.tm08532>.

This package provides user-friendly tools for calibration in survey sampling. The package is production-oriented, and its interface is inspired by the famous popular macro Calmar for SAS, so that Calmar users can quickly get used to icarus'. In addition to calibration (with linear, raking and logit methods), icarus features functions for calibration on tight bounds and penalized calibration.

Compute distributional quantities for an Integrated Gamma (IG) or Integrated Gamma Limit (IGL) copula, such as a cdf and density. Compute corresponding conditional quantities such as the cdf and quantiles. Generate data from an IG or IGL copula. See the vignette for formulas, or for a derivation, see Coia, V (2017) "Forecasting of Nonlinear Extreme Quantiles Using Copula Models." PhD Dissertation, The University of British Columbia.

This package provides functions to compute and plot Krippendorff's inter-coder reliability coefficient alpha and bootstrapped uncertainty estimates (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to make use of parallel threads where supported.

One function to read files. One function to write files. One function to direct plots to screen or file. Automatic file format inference and directory structure creation.

An open source library for face detection in images. Provides a pretrained convolutional neural network based on <https://github.com/ShiqiYu/libfacedetection> which can be used to detect faces which have size greater than 10x10 pixels.

Launches a shiny based application for Nuclear Magnetic Resonance (NMR)data importation and Statistical TOtal Correlation SpectroscopY (STOCSY) analyses in a full interactive approach. The theoretical background and applications of STOCSY method could be found at Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E. & Nicholson, J. (2005) <doi:10.1021/ac048630x>.

This package implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.

Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., Connection Weights described by Olden et al. (2004) <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance propagation ('LRP') described by Bach et al. (2015) <doi:10.1371/journal.pone.0130140>, deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) <doi:10.48550/arXiv.1704.02685> and gradient-based methods like SmoothGrad described by Smilkov et al. (2017) <doi:10.48550/arXiv.1706.03825>, Gradient x Input or Vanilla Gradient'. Details can be found in the accompanying scientific paper: Koenen & Wright (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).

The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.

This package implements approximate Bayesian inference for Structural Equation Models (SEM) using a custom adaptation of the Integrated Nested Laplace Approximation as described in Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x>. Provides a computationally efficient alternative to Markov Chain Monte Carlo (MCMC) for Bayesian estimation, allowing users to fit latent variable models using the lavaan syntax.

Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) <doi:10.1093/bioinformatics/btx750> and Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.

Computes intervention in prediction measure for assessing variable importance for random forests. See details at I. Epifanio (2017) <DOI:10.1186/s12859-017-1650-8>.

Know which loop iteration the code execution is up to by including a single, convenient function call inside the loop.

This package provides fast application of image filters to data matrices, using R and C++ algorithms.

This package provides a straightforward interface for accessing the IMF (International Monetary Fund) data JSON API, available at <https://data.imf.org/>. This package offers direct access to the primary API endpoints: Dataflow, DataStructure, and CompactData. And, it provides an intuitive interface for exploring available dimensions and attributes, as well as querying individual time-series datasets. Additionally, the package implements a rate limit on API calls to reduce the chances of exceeding service limits (limited to 10 calls every 5 seconds) and encountering response errors.

Methodology for subgroup selection in the context of isotonic regression including methods for sub-Gaussian errors, classification, homoscedastic Gaussian errors and quantile regression. See the documentation of ISS(). Details can be found in the paper by Müller, Reeve, Cannings and Samworth (2023) <arXiv:2305.04852v2>.

This package provides tools for estimating uncertainty in individual polygenic risk scores (PRSs) using both sampling-based and analytical methods, as well as the Best Linear Unbiased Estimator (BLUE). These methods quantify variability in PRS estimates for both binary and quantitative traits. See Henderson (1975) <doi:10.2307/2529430> for more details.

This package provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.

This package provides functions to perform robust nonparametric survival analysis with right censored data using a prior near-ignorant Dirichlet Process. Mangili, F., Benavoli, A., de Campos, C.P., Zaffalon, M. (2015) <doi:10.1002/bimj.201500062>.

Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) <doi:10.18637/jss.v096.i03>.

We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) <doi:10.1093/bioinformatics/btaa017>.