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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

5.02 score 5 stars 1 dependents 3 scripts 520 downloads

funcml - 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

4.54 score 355 downloads

functionals - 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

4.48 score 1 stars 2 dependents 148 downloads

unsurv - Unsupervised Clustering of Individualized Survival Curves

Tools for clustering individualized survival curves using the Partitioning Around Medoids (PAM) algorithm, with monotonic enforcement, optional smoothing, weighted distances (L1/L2), automatic K selection via silhouette width, prediction for new curves, basic stability checks, and plotting helpers. The clustering strategy follows Kaufman and Rousseeuw (1990, ISBN:0471878766).

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4.30 score 472 downloads

survalis - Interpretable Survival Machine Learning Framework

A modular toolkit for interpretable survival machine learning with a unified interface for fitting, prediction, evaluation, and interpretation. It includes semiparametric, parametric, tree-based, ensemble, boosting, kernel, and deep-learning survival learners, together with benchmarking, scoring, calibration, and model-agnostic interpretation utilities. Representative methodological anchors include Cox (1972) <doi:10.1111/j.2517-6161.1972.tb00899.x>, Royston and Parmar (2002) <doi:10.1002/sim.1203>, Ishwaran et al. (2008) <doi:10.1214/08-AOAS169>, Jaeger et al. (2019) <doi:10.1214/19-AOAS1261>, Harrell et al. (1982) <doi:10.1001/jama.1982.03320430047030>, Graf et al. (1999) <doi:10.1002/(SICI)1097-0258(19990915/30)18:17/18%3C2529::AID-SIM274%3E3.0.CO;2-5>, Friedman (2001) <doi:10.1214/aos/1013203451>, Apley and Zhu (2020) <doi:10.1111/rssb.12377>, and Lundberg and Lee (2017) <https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions>, and other related methods for survival modeling, prediction, and interpretation.

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interpretable-machine-learningsurvival-analysis

4.08 score 3 scripts 512 downloads

tvrmst - Time-Varying Restricted Mean Survival Time from Survival Matrices

Utilities for restricted mean survival time (RMST) and time-varying restricted mean survival time quantities computed from survival curves provided on a time grid. The package is model-agnostic and accepts only a time vector and survival matrices, returning RMST-based quantities and bootstrap summaries. For restricted mean survival time methodology, see Royston and Parmar (2013) <doi:10.1186/1471-2288-13-152>.

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3.18 score 479 downloads

mcstatsim - 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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3.00 score 2 stars 9 scripts 143 downloads

missCforest - Ensemble Conditional Trees for Missing Data Imputation

Single imputation based on the Ensemble Conditional Trees (i.e. Cforest algorithm Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007) <doi:10.1186/1471-2105-8-25>).

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2.70 score 4 scripts 227 downloads