Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.

DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015). Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions. Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) <doi:10.1016/j.cogpsych.2015.02.005>. Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs), a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008). Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common features from within standard diffusion models with the addition of superimposed controlled and automatic activation. The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality to fit the model to observed data. Further details are provided in the following paper: Mackenzie, I.G., & Dudschig, C. (2021). DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction time and error rate data. Methods in Psychology, 100074. <doi:10.1016/j.metip.2021.100074>.

This package implements the de-biased estimator for low-rank matrix completion and provides confidence intervals for entries of interest. See: by Chen et al. (2019) <doi:10.1073/pnas.1910053116>, Mai (2021) <arXiv:2103.11749>.

This package provides tools for constructing, manipulating and using distance metrics.

Create and customize interactive collapsible D3 trees using the D3 JavaScript library and the htmlwidgets package. These trees can be used directly from the R console, from RStudio', in Shiny apps and R Markdown documents. When in Shiny the tree layout is observed by the server and can be used as a reactive filter of structured data.

Fits dose-response models utilizing a Bayesian model averaging approach as outlined in Gould (2019) <doi:10.1002/bimj.201700211> for both continuous and binary responses. Longitudinal dose-response modeling is also supported in a Bayesian model averaging framework as outlined in Payne, Ray, and Thomann (2024) <doi:10.1080/10543406.2023.2292214>. Functions for plotting and calculating various posterior quantities (e.g. posterior mean, quantiles, probability of minimum efficacious dose, etc.) are also implemented. Copyright Eli Lilly and Company (2019).

This package provides a collection of tests to analyze the causal direction of dependence in linear models (Wiedermann, W., & von Eye, A., 2025, ISBN: 9781009381390). The package includes functions to perform Direction Dependence Analysis for variable distributions, residual distributions, and independence properties of predictors and residuals in competing causal models. In addition, the package contains functions to test the causal direction of dependence in conditional models (i.e., models with interaction terms) For more information see <https://www.ddaproject.com>.

Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.

This package implements an algorithm to effortlessly split a column in an R data frame filled with multiple values separated by delimiters. This automates the process of creating separate columns for each unique value, transforming them into binary outcomes.

This package provides a dibble that implements data cubes (derived from dimensional tibble'), and allows broadcasting by dimensional names.

Computes discrete fast Fourier transform of river discharge data and the derived metrics. The methods are described in J. L. Sabo, D. M. Post (2008) <doi:10.1890/06-1340.1> and J. L. Sabo, A. Ruhi, G. W. Holtgrieve, V. Elliott, M. E. Arias, P. B. Ngor, T. A. Räsänsen, S. Nam (2017) <doi:10.1126/science.aao1053>.

Simple feature stores and tools for creating personalised feature stores. diseasystore powers feature stores which can automatically link and aggregate features to a given stratification level. These feature stores are automatically time-versioned (powered by the SCDB package) and allows you to easily and dynamically compute features as part of your continuous integration.

This package provides several data sets for use with discrete statistical tests and discrete multiple testing procedures. Some of them are also available as a four-column version, so that each row represents a 2x2 table.

An R package for iterative and batched record linkage, and applying epidemiological case definitions. diyar can be used for deterministic and probabilistic record linkage, or multistage record linkage combining both approaches. It features the implementation of nested match criteria, and mechanisms to address missing data and conflicting matches during stepwise record linkage. Case definitions are implemented by assigning records to groups based on match criteria such as person or place, and overlapping time or duration of events e.g. sample collection dates or periods of hospital stays. Matching records are assigned a unique group ID. Index and duplicate records are removed or further analyses as required.

This package provides a comprehensive toolkit for analyzing microscopy data output from QuPath software. Provides functionality for automated data processing, metadata extraction, and statistical analysis of imaging results. The methodology implemented in this package is based on Labrosse et al. (2024) <doi:10.1016/j.xpro.2024.103274> "Protocol for quantifying drug sensitivity in 3D patient-derived ovarian cancer models", which describes the complete workflow for drug sensitivity analysis in patient-derived cancer models.

Add a "Did You Mean" feature to the R interactive. With this package, error messages for misspelled input of variable names or package names suggest what you really want to do in addition to notification of the mistake.

This package provides a datetime range picker widget for usage in Shiny'. It creates a calendar allowing to select a start date and an end date as well as two fields allowing to select a start time and an end time.

This package provides a set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings. Compatible with and based on the R grid package.

Implementation of selected Tidyverse functions within DataSHIELD', an open-source federated analysis solution in R. Currently, DataSHIELD contains very limited tools for data manipulation, so the aim of this package is to improve the researcher experience by implementing essential functions for data manipulation, including subsetting, filtering, grouping, and renaming variables. This is the clientside package which should be installed locally, and is used in conjuncture with the serverside package dsTidyverse which is installed on the remote server holding the data. For more information, see <https://tidyverse.org/> and <https://datashield.org/>.

Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios. The purpose of the package is to help individuals learn about infectious disease epidemiology (ecology/evolution) from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models.

Fit a Poisson regression to carcass distance data and integrate over the searched area at a wind farm to estimate the fraction of carcasses falling in the searched area and format the output for use as the dwp parameter in the GenEst or eoa package for estimating bird and bat mortality, following Dalthorp, et al. (2022) <arXiv:2201.10064>.

Dynamic stochastic block model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time, developed in Matias and Miele (2016) <doi:10.1111/rssb.12200>.

The disparity filter algorithm is a network reduction technique to identify the backbone structure of a weighted network without destroying its multi-scale nature. The algorithm is documented in M. Angeles Serrano, Marian Boguna and Alessandro Vespignani in "Extracting the multiscale backbone of complex weighted networks", Proceedings of the National Academy of Sciences 106 (16), 2009. This implementation of the algorithm supports both directed and undirected networks.

Explore neural networks in a layer oriented way, the framework is intended to give the user total control of the internals of a net without much effort. Use classes like PerceptronLayer to create a layer of Percetron neurons, and specify how many you want. The package does all the tricky stuff internally leaving you focused in what you want. I wrote this package during a neural networks course to help me with the problem set.