Data manipulation in one package and in base R. Minimal. No dependencies. dplyr and tidyr'-like in one place. Nothing else than base R to build the package.

This package provides routines to compute normalised prediction distribution errors, a metric designed to evaluate non-linear mixed effect models such as those used in pharmacokinetics and pharmacodynamics.

This package provides functions for fitting a sparse partial least squares (SPLS) regression and classification (Chun and Keles (2010) <doi:10.1111/j.1467-9868.2009.00723.x>).

Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.

Makes research involving EMDAT and related datasets easier. These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions.

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 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).

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 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.

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.

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.

This package provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R., and Gupta, V. K. (2013) <doi:10.1007/s40003-013-0059-5>.

Implementation of an alternating direction method of multipliers algorithm for fitting a linear model with tree-based lasso regularization, which is proposed in Algorithm 1 of Yan and Bien (2020) <doi:10.1080/01621459.2020.1796677>. The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free.

Generates pseudo-random vectors that follow an arbitrary von Mises-Fisher distribution on a sphere. This method is fast and efficient when generating a large number of pseudo-random vectors. Functions to generate random variates and compute density for the distribution of an inner product between von Mises-Fisher random vector and its mean direction are also provided. Details are in Kang and Oh (2024) <doi:10.1007/s11222-024-10419-3>.

Schema definitions and read, write and validation tools for data formatted in accordance with the AIRR Data Representation schemas defined by the AIRR Community <http://docs.airr-community.org>.

This package provides a wrapper for ada-url', a WHATWG compliant and fast URL parser written in modern C++'. Also contains auxiliary functions such as a public suffix extractor.

This package provides functions for Maximum Likelihood Estimation, Markov Chain Monte Carlo, finding confidence intervals. The implementation is heavily based on the original Fortran source code translated to R.

This package provides a toolkit for computing and visualizing CAPL-2 (Canadian Assessment of Physical Literacy, Second Edition; <https://www.capl-eclp.ca>) scores and interpretations from raw data.

Estimating the number of factors in Exploratory Factor Analysis (EFA) with out-of-sample prediction errors using a cross-validation scheme. Haslbeck & van Bork (Preprint) <https://psyarxiv.com/qktsd>.

Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://scienceverse.github.io/faux/>. Described in DeBruine (2020) <doi:10.5281/zenodo.2669586>.

This package implements a path algorithm for the Fused Lasso Signal Approximator. For more details see the help files or the article by Hoefling (2009) <arXiv:0910.0526>.

The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.