This package performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI).

We provide functionality to implement penalized PCA with an option to smooth the objective function using Nesterov smoothing. Two functions are available to compute a user-specified number of eigenvectors. The function unsmoothed_penalized_EV() computes a penalized PCA without smoothing and has three parameters (the input matrix, the Lasso penalty, and the number of desired eigenvectors). The function smoothed_penalized_EV() computes a smoothed penalized PCA using the same parameters and additionally requires the specification of a smoothing parameter. Both functions return a matrix having the desired eigenvectors as columns.

This package provides functions to create and manage research compendiums for data analysis. Research compendiums are a standard and intuitive folder structure for organizing the digital materials of a research project, which can significantly improve reproducibility. The package offers several compendium structure options that fit different research project as well as the ability of duplicating the folder structure of existing projects or implementing custom structures. It also simplifies the use of version control.

Implementation of the SRCS method for a color-based visualization of the results of multiple pairwise tests on a large number of problem configurations, proposed in: I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.

This package provides functions to estimate, predict and interpolate areal data. For estimation and prediction we assume areal data is an average of an underlying continuous spatial process as in Moraga et al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020) <doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020) <doi:10.1093/biostatistics/kxy041>. The interpolation methodology is (mostly) based on Goodchild and Lam (1980, ISSN:01652273).

This package provides a small set of helper functions to convert sjPlot HTML-tables to R data.frame objects / knitr::kable-tables.

This package provides functions to nonparametrically assess assumptions necessary to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of regression functions, and non-negative residual treatment effects. More details are available in Hsiao et al 2025 (under review). A tutorial for this package can be found at <https://laylaparast.com/home/SurrogateParadoxTest.html>.

This package creates D3 JavaScript scatterplots from R with interactive features : panning, zooming, tooltips, etc.

One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. singleCellHaystack predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. singleCellHaystack uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of singleCellHaystack we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez (Scientific Reports, 2023) <doi:10.1038/s41598-023-38965-2>.

This package provides a comprehensive suite of portfolio spanning tests for asset pricing, such as Huberman and Kandel (1987) <doi:10.1111/j.1540-6261.1987.tb03917.x>, Gibbons et al. (1989) <doi:10.2307/1913625>, Kempf and Memmel (2006) <doi:10.1007/BF03396737>, Pesaran and Yamagata (2024) <doi:10.1093/jjfinec/nbad002>, and Gungor and Luger (2016) <doi:10.1080/07350015.2015.1019510>.

Gives design points from a sequential full factorial-based Latin hypercube design, as described in Duan, Ankenman, Sanchez, and Sanchez (2015, Technometrics, <doi:10.1080/00401706.2015.1108233>).

An enterprise-targeted scalable and customizable shiny module providing an easy way to incorporate free-form note taking or discussion boards into applications. The package includes a shiny module that can be included in any shiny application to create a panel containing searchable, editable text broken down by section headers. Can be used with a local SQLite database, or a compatible remote database of choice.

This package provides tools to simulate and analyse datasets of social interactions between individuals using hierarchical Bayesian models implemented in Stan. Model fitting is performed via the rstan package. Users can generate realistic interaction data where individual phenotypes influence and respond to those of their partners, with control over sampling design parameters such as the number of individuals, partners, and repeated dyads. The simulation framework allows flexible control over variation and correlation in mean trait values, social responsiveness, and social impact, making it suitable for research on interacting phenotypes and on direct and indirect genetic effects ('DGEs and IGEs'). The package also includes functions to fit and compare alternative models of social effects, including impactâ responsiveness, varianceâ partitioning, and trait-based models, and to summarise model performance in terms of bias and dispersion. For a more detailed description of the available models and impactâ responsiveness, see the accompanying preprint Wijnhorst et al. (2025) <doi:10.32942/X2F65M>.

An object oriented framework to simulate ecological (and other) dynamic systems. It can be used for differential equations, individual-based (or agent-based) and other models as well. It supports structuring of simulation scenarios (to avoid copy and paste) and aims to improve readability and re-usability of code.

Design, build, and deploy R packages demo presentations by an interactive wizard. Set up unique title, logo and themes. Add personalized tabs exposing applicability. And deploy as a part of a package or an independent app.

Terrestrial and marine predictors for species distribution modelling from multiple sources, including WorldClim <https://www.worldclim.org/>,, ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/> and MARSPEC <http://www.marspec.org/>.

This package provides a critical first step in systematic literature reviews and mining of academic texts is to identify relevant texts from a range of sources, particularly databases such as Web of Science or Scopus'. These databases often export in different formats or with different metadata tags. synthesisr expands on the tools outlined by Westgate (2019) <doi:10.1002/jrsm.1374> to import bibliographic data from a range of formats (such as bibtex', ris', or ciw') in a standard way, and allows merging and deduplication of the resulting dataset.

This package provides a set of functions for generating SPSS syntax files from the R environment.

This package provides modular functions and applications for quickly generating plots and tables. Each modular function opens a graphical user interface providing the user with options to create and customise plots and tables.

This package provides a port of the Scarabee toolkit originally written as a Matlab-based application. scaRabee provides a framework for simulation and optimization of pharmacokinetic-pharmacodynamic models at the individual and population level. It is built on top of the neldermead package, which provides the direct search algorithm proposed by Nelder and Mead for model optimization.

This package provides tools to import and export from several existing pieces of ion-channel analysis software such as TAC', QUB', SCAN', and Clampfit', implements procedures such as dwell-time correction and defining bursts with a critical time, and provides tools for analysis of bursts, such as tools for sorting and plotting.

Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies.

This package provides a collection of functions to search and download street view imagery ('Mapilary <https://www.mapillary.com/developer/api-documentation>) and to extract, quantify, and visualize visual features. Moreover, there are functions provided to generate Qualtrics survey in TXT format using the collection of street views for various research purposes.

This package provides a pipeline to perform small area estimation and prevalence mapping of binary indicators using health and demographic survey data, described in Fuglstad et al. (2022) <doi:10.48550/arXiv.2110.09576> and Wakefield et al. (2020) <doi:10.1111/insr.12400>.