This package performs Correlated Meta-Analysis ('corrmeta') across multiple OMIC scans, accounting for hidden non-independencies between elements of the scans due to overlapping samples, related samples, or other information. For more information about the method, refer to the paper Province MA. (2013) <doi:10.1142/9789814447973_0023>.

Helps automate Quarto website creation for small academic groups. Builds a database-like structure of people, projects and publications, linking them together with a string-based ID system. Then, provides functions to automate production of clean markdown for these structures, and in-built CSS formatting using CSS flexbox.

This package provides friendly wrappers for creating duckdb'-backed connections to tabular datasets ('csv', parquet, etc) on local or remote file systems. This mimics the behaviour of "open_dataset" in the arrow package, but in addition to S3 file system also generalizes to any list of http URLs.

Simplifies some complicated and labor intensive processes involved in exploring and explaining data. Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data. Also includes some convenience features designed to remove as much redundant typing as possible.

This package provides a collection of curated educational datasets for teaching ecology and agriculture concepts. Includes data on wildlife monitoring, plant treatments, and ecological observations with documentation and examples for educational use. All datasets are derived from published scientific studies and are available under CC0 or compatible licenses.

Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

An RStudio addin for editing a data.frame or a tibble'. You can delete, add or update a data.frame without coding. You can get resultant data as a data.frame'. In the package, modularized shiny app codes are provided. These modules are intended for reuse across applications.

This package provides a tool for conducting exact parametric regression-based causal mediation analysis of binary outcomes as described in Samoilenko, Blais and Lefebvre (2018) <doi:10.1353/obs.2018.0013>; Samoilenko, Lefebvre (2021) <doi:10.1093/aje/kwab055>; and Samoilenko, Lefebvre (2023) <doi:10.1002/sim.9621>.

This package implements stochastic simulations of community assembly (ecological diversification) using customizable ecospace frameworks (functional trait spaces). Provides a wrapper to calculate common ecological disparity and functional ecology statistical dynamics as a function of species richness. Functions are written so they will work in a parallel-computing environment.

Optimal experimental designs for functional linear and functional generalised linear models, for scalar responses and profile/dynamic factors. The designs are optimised using the coordinate exchange algorithm. The methods are discussed by Michaelides (2023) <https://eprints.soton.ac.uk/474982/1/Thesis_DamianosMichaelides_Final_pdfa_1_.pdf>.

This package provides a set of helper functions for constructing file paths relative to the root of various types of projects, such as R packages, Git repositories, and more. File paths are specified with function arguments, or `$` to navigate into folders to specific files supported by auto-completion.

An R interface to the GPTZero API (<https://gptzero.me/docs>). Allows users to classify text into human and computer written with probabilities. Formats the data into data frames where each sentence is an observation. Paragraph-level and document-level predictions are organized to align with the sentences.

This package provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication.

Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) <doi:10.1002/bimj.202300078>. See Skarstein and Muff (2024) <doi:10.48550/arXiv.2406.08172> for details on using the package.

Evaluates whether the relationship between two vectors is linear or nonlinear. Performs a test to determine how well a linear model fits the data compared to higher order polynomial models. Jhang et al. (2004) <doi:10.1043/1543-2165(2004)128%3C44:EOLITC%3E2.0.CO;2>.

The localLLM package provides R bindings to the llama.cpp library for running large language models. The package uses a lightweight architecture where the C++ backend library is downloaded at runtime rather than bundled with the package. Package features include text generation, reproducible generation, and parallel inference.

Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2019) <doi:10.1080/01621459.2018.1520115>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.

Insieme di funzioni di supporto al volume "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. This package contains sets of functions defined in "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. Function names and docs are in italian as well.

Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using Stan'.

Collection of functions to compute within-study covariances for different effect sizes, data visualization, and single and multiple imputations for missing data. Effect sizes include correlation (r), mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD).

This package performs nonparametric estimation in mixture cure models when the cure status is partially known. For details, see Safari et al (2021) <doi:10.1002/bimj.202100156>, Safari et al (2022) <doi:10.1177/09622802221115880> and Safari et al (2023) <doi:10.1007/s10985-023-09591-x>.

Estimate penalized synthetic control models and perform hold-out validation to determine their penalty parameter. This method is based on the work by Abadie & L'Hour (2021) <doi:10.1080/01621459.2021.1971535>. Penalized synthetic controls smoothly interpolate between one-to-one matching and the synthetic control method.

Conduct power analyses and inference of marginal effects. Uses plug-in estimation and influence functions to perform robust inference, optionally leveraging historical data to increase precision with prognostic covariate adjustment. The methods are described in Højbjerre-Frandsen et al. (2025) <doi:10.48550/arXiv.2503.22284>.

Different methods for PLS analysis of one or two data tables such as Tucker's Inter-Battery, NIPALS, SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis. The main reference for this software is the awesome book (in French) La Regression PLS: Theorie et Pratique by Michel Tenenhaus.