survdnn - Deep Neural Networks for Survival Analysis with R 'torch'
Provides deep learning models for right-censored survival data using the 'torch' backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>.
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deep-neural-networkssurvival-analysis
5.08 score 5 stars 1 dependents 3 scripts 297 downloadsCEACT - Cost-Effectiveness Analysis Toolkit for Clinical Trials
Provides tools for trial-based economic evaluation of healthcare interventions. Computes and visualizes incremental cost-effectiveness ratios, cost-effectiveness acceptability curves, cost-effectiveness planes, net monetary benefit tables, and one-way deterministic sensitivity analyses. Supports cost-utility analyses using observed summaries and non-parametric bootstrap uncertainty.
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clinical-trialshealth-economic-evaluation
4.85 score 7 stars 388 downloadsfuncml - Functional Machine Learning Framework
A compact and explicit machine learning framework for supervised learning, resampling-based evaluation, hyperparameter tuning, learner comparison, interpretation, and plug-in g-computation. The package uses standard formulas for model specification and provides stable S3 interfaces for fitting, evaluation, tuning, interpretation, and causal estimation across a learner registry with multiple backend engines. Implemented interpretation methods build on established approaches such as permutation-based variable importance, partial dependence, individual conditional expectation, accumulated local effects, SHAP, and LIME; see Friedman (2001) <doi:10.1214/aos/1013203451>, Goldstein et al. (2015) <doi:10.1080/10618600.2014.907095>, Apley and Zhu (2020) <doi:10.1111/rssb.12377>, Lundberg and Lee (2017) <doi:10.48550/arXiv.1705.07874>, and Ribeiro et al. (2016) <doi:10.48550/arXiv.1602.04938>. The framework is intentionally opinionated: preprocessing is expected to occur outside the modeling step, and the API emphasizes explicit inputs, consistent object contracts, and compact interfaces rather than feature-by-feature competition with larger machine learning ecosystems.
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classificationg-computationinterpretable-machine-learningmachine-learningregressiontabular-data
4.60 score 1 stars 375 downloadsfunctionals - Functional Programming with Parallelism and Progress Tracking
Provides functional tools such as fmap(), fwalk(), and fapply() to iterate over vectors, data frames, or grouped data with optional parallelism and real-time progress tracking. Progress updates now reflect completed tasks across sequential, multicore, and cluster-backed execution. Designed for readable and reproducible workflows, including support for Monte Carlo simulations and benchmarking.
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functional-programming
4.43 score 1 stars 3 dependents 238 downloadsmimar - Compact Multiple Imputation, Assessment, and Reporting
Provides compact tools for missing-data analysis, including artificial amputation, chained single and multiple imputation, statistical and machine-learning-based imputation methods, diagnostic evaluation, and post-imputation pooling.
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data-analysisimputationmachine-learningmissing-datamultiple-imputationstatistics
4.40 score 209 downloadsmcstatsim - Monte Carlo Statistical Simulation Tools Using a Functional Approach
A lightweight package designed to facilitate statistical simulations through functional programming. It centralizes the simulation process into a single higher-order function, enhancing manageability and usability without adding overhead from external dependencies. The package includes ready-to-use functions for common simulation targets. A detailed example can be found on <https://github.com/ielbadisy/mcstatsim>.
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3.20 score 2 stars 16 scripts 203 downloads