Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods. Tervonen, T. and Figueira, J. R. (2008) <doi:10.1002/mcda.407>.

UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge.

This package proposes a new file format named gson for storing gene set and related information, and provides read, write and other utilities to process this file format.

This package provides an integrated set of functions for the analysis of multivariate normal datasets with missing values, including implementation of the EM algorithm, data augmentation, and multiple imputation.

This package provides an implementation of interpreted string literals, inspired by Python's Literal String Interpolation (PEP-0498) and Docstrings (PEP-0257) and Julia's Triple-Quoted String Literals.

This is a package for model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).

r-kmer is an R package for rapidly computing distance matrices and clustering large sequence datasets using fast alignment-free k-mer counting and recursive k-means partitioning.

This package provides the Rakarrack effects as LV2 plugins. The ports are done such that hopefully when Rakarrack gets an active maintainer these will get merged into the original project.

Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of the least trimmed square loss of the models in the ensembles under constraints for the size of the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and diversity of the robust ensemble are selected by cross-validation.

Implementation of the Robust Exponential Decreasing Index (REDI), proposed in the article by Issa Moussa, Arthur Leroy et al. (2019) <https://bmjopensem.bmj.com/content/bmjosem/5/1/e000573.full.pdf>. The REDI represents a measure of cumulated workload, robust to missing data, providing control of the decreasing influence of workload over time. Various functions are provided to format data, compute REDI, and visualise results in a simple and convenient way.

Personalized assignment to one of many treatment arms via regularized and clustered joint assignment forests as described in Ladhania, Spiess, Ungar, and Wu (2023) <doi:10.48550/arXiv.2311.00577>. The algorithm pools information across treatment arms: it considers a regularized forest-based assignment algorithm based on greedy recursive partitioning that shrinks effect estimates across arms; and it incorporates a clustering scheme that combines treatment arms with consistently similar outcomes.

St implements a simple and lightweight terminal emulator. It implements 256 colors, most VT10X escape sequences, utf8, X11 copy/paste, antialiased fonts (using fontconfig), fallback fonts, resizing, and line drawing.

Estimate diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where can not measure the golden standard but can estimate it using the attributable fraction.

This package provides the source and examples for James P. Howard, II, "Computational Methods for Numerical Analysis with R," <https://jameshoward.us/cmna/>, a book on numerical methods in R.

This package performs least squares constrained optimization on a linear objective function. It contains a number of algorithms to choose from and offers a formula syntax similar to lm().

Bayesian networks with continuous and/or discrete variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.

Fit logistic functions to observed dose-response continuous data and evaluate goodness-of-fit measures. See Malyutina A., Tang J., and Pessia A. (2023) <doi:10.18637/jss.v106.i04>.

Statistical and graphical tools for detecting and measuring discrimination and bias, be it racial, gender, age or other. Detection and remediation of bias in machine learning algorithms. Python interfaces available.

This package contains data sets, examples and software from the Second Edition of "Design of Observational Studies"; see Rosenbaum, P.R. (2010) <doi:10.1007/978-1-4419-1213-8>.

Background correction of spectral like data. Handles variations in scaling, polynomial baselines, interferents, constituents and replicate variation. Parameters for corrections are stored for further analysis, and spectra are corrected accordingly.

This package provides tools for fluctuations analysis of mutant cells counts. Main reference is A. Mazoyer, R. Drouilhet, S. Despreaux and B. Ycart (2017) <doi:10.32614/RJ-2017-029>.

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations errors association, endogeneity, non-random sample selection or partial observability.

This package implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).

This package provides a set of routines to quickly download and import the HGNC data set on mapping of gene symbols to gene entries in other popular databases or resources.