Analysis of multi environment data of plant breeding experiments following the analyses described in Malosetti, Ribaut, and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package asreml for the ASReml software, which can be obtained upon purchase from VSN international (<https://vsni.co.uk/software/asreml-r/>).

Bundled example datasets used by the SyncER package vignette and test suite: synthetic radiocarbon-dated event records for five cores and the corresponding completed rbacon'/'rplum age-depth model output (raw, synchronized, and synchronized-without-14C variants). These data let SyncER demonstrate and test its full workflow reproducibly, without requiring users to install rbacon'/'rplum or re-run Bayesian age-depth modelling. This package contains the synthetic data from Wils & Ramisch (2026) <doi:10.1038/s41598-026-67943-7>.

An interactive framework for simulating blockchain protocols using a hybrid R-Shiny and Python architecture. The package provides tools to visualize peer-to-peer network maps, manage supply chain logistics on-chain, and execute cross-border settlements via smart contract logic. It leverages the reticulate package to perform standardized cryptographic operations, including SHA-256 hashing, Merkle Tree construction, and ECDSA (Elliptic Curve Digital Signature Algorithm) key generation. This tool is designed for pedagogical demonstration and rapid prototyping of distributed ledger requirements.

Given a list of substance compositions, a list of substances involved in a process, and a list of constraints in addition to mass conservation of elementary constituents, the package contains functions to build the substance composition matrix, to analyze the uniqueness of process stoichiometry, and to calculate stoichiometric coefficients if process stoichiometry is unique. (See Reichert, P. and Schuwirth, N., A generic framework for deriving process stoichiometry in enviromental models, Environmental Modelling and Software 25, 1241-1251, 2010 for more details.).

This package produces publication-quality LaTeX, ASCII, and HTML regression tables. Designed as a drop-in replacement for the stargazer package for lm and glm models, with added native support for fixest', plm', and alpaca objects including automatic fixed-effect and random-effect indicator rows and SE-type detection. Standard errors can be supplied as variance-covariance matrices, numeric vectors, or auto-extracted from supported model objects. Follows the interface of Hlavac (2022) <https://CRAN.R-project.org/package=stargazer>.

This package provides a simple, configurable framework for OpenID Connect (OIDC) authentication and OAuth 2.0 authorization in shiny applications using S7 classes. Defines providers, clients, and tokens, as well as various supporting functions and a shiny module. Features include cross-site request forgery (CSRF) protection, state encryption, Proof Key for Code Exchange (PKCE) handling, validation of OIDC identity tokens (nonces, signatures, claims), automatic user info retrieval for OIDC and supported OAuth providers, asynchronous flows, and hooks for audit logging.

This package provides a collection of functions related to novel methods for estimating R(t), created by the lab of Professor Laura White. Currently implemented methods include two-step Bayesian back-calculation and now-casting for line-list data with missing reporting delays, adapted in STAN from Li (2021) <doi:10.1371/journal.pcbi.1009210>, and calculation of time-varying reproduction number assuming a flux between various adjacent states, adapted into STAN from Zhou (2021) <doi:10.1371/journal.pcbi.1010434>.

This package provides a new batch effect correction method based on Projection to Latent Structures Discriminant Analysis named “PLSDA-batch” to correct data prior to any downstream analysis. PLSDA-batch estimates latent components related to treatment and batch effects to remove batch variation. The method is multivariate, non-parametric and performs dimension reduction. Combined with centered log ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far.

This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. CatsCradle allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes.

Supporting data for the ELMER package. It includes: - elmer.data.example.promoter: mae.promoter - elmer.data.example: data - EPIC.hg38.manifest - EPIC.hg19.manifest - hm450.hg38.manifest - hm450.hg19.manifest - hocomoco.table - human.TF - LUSC_meth_refined: Meth - LUSC_RNA_refined: GeneExp - Probes.motif.hg19.450K - Probes.motif.hg19.EPIC - Probes.motif.hg38.450K - Probes.motif.hg38.EPIC - TF.family - TF.subfamily - Human_genes__GRCh37_p13 - Human_genes__GRCh38_p12 - Human_genes__GRCh37_p13__tss - Human_genes__GRCh38_p12__tss.

This package provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from JAGS and Stan models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating JAGS and bridgesampling syntax to basic functions such as rng, quantile, and distribution functions.

Given the hypothesis of a bi-modal distribution of cells for each marker, the algorithm constructs a binary tree, the nodes of which are subpopulations of cells. At each node, observed cells and markers are modeled by both a family of normal distributions and a family of bi-modal normal mixture distributions. Splitting is done according to a normalized difference of AIC between the two families. Method is detailed in: Commenges, Alkhassim, Gottardo, Hejblum & Thiebaut (2018) <doi: 10.1002/cyto.a.23601>.

This package implements the Dyad Ratios algorithm for estimating latent variables from time-series survey data. The algorithm estimates a latent mood dimension (or two dimensions) from a set of issue opinion series. Supports annual, quarterly, monthly, and daily aggregation intervals, optional exponential smoothing, and up to two latent dimensions. Input data can be provided as a data frame or read from delimited text files. Based on Stimson's MCalc C++ program. See Stimson (2018) <doi:10.1177/0759106318761614> for more details.

This package provides a toolbox for constructing potential landscapes for Ising networks. The parameters of the networks can be directly supplied by users or estimated by the IsingFit package by van Borkulo and Epskamp (2016) <https://CRAN.R-project.org/package=IsingFit> from empirical data. The Ising model's Boltzmann distribution is preserved for the potential landscape function. The landscape functions can be used for quantifying and visualizing the stability of network states, as well as visualizing the simulation process.

This package performs multilevel matches for data with cluster- level treatments and individual-level outcomes using a network optimization algorithm. Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis. Details in Pimentel et al. (2018) <doi:10.1214/17-AOAS1118>. The optmatch package, which is useful for running many of the provided functions, may be downloaded from Github at <https://github.com/markmfredrickson/optmatch> if not available on CRAN.

This package provides a switch-case construct for R', as it is known from other programming languages. It allows to test multiple, similar conditions in an efficient, easy-to-read manner, so nested if-else constructs can be avoided. The switch-case construct is designed as an R function that allows to return values depending on which condition is met and lets the programmer flexibly decide whether or not to leave the switch-case construct after a case block has been executed.

This package provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results.

Apply and visualize conditional formatting to data frames in R. It renders a data frame with cells formatted according to criteria defined by rules, using a tidy evaluation syntax. The table is printed either opening a web browser or within the RStudio viewer if available. The conditional formatting rules allow to highlight cells matching a condition or add a gradient background to a given column. This package supports both HTML and LaTeX outputs in knitr reports, and exporting to an xlsx file.

Whole-buffer DEFLATE-based compression and decompression of raw vectors using the libdeflate library (see <https://github.com/ebiggers/libdeflate>). Provides the user with additional control over the speed and the quality of DEFLATE compression compared to the fixed level of compression offered in R's memCompress() function. Also provides the libdeflate static library and C headers along with a CMake target and packageâ config file that ease linking of libdeflate in packages that compile and statically link bundled libraries using CMake'.

This package provides functions to support compatibility between Maelstrom R packages and Opal environment. Opal is the OBiBa core database application for biobanks. It is used to build data repositories that integrates data collected from multiple sources. Opal Maelstrom is a specific implementation of this software. This Opal client is specifically designed to interact with Opal Maelstrom distributions to perform operations on the R server side. The user must have adequate credentials. Please see <https://opaldoc.obiba.org/> for complete documentation.

Allows the user to perform ANOVA tests (in a strict sense: continuous and normally-distributed Y variable and 1 or more factorial/categorical X variable(s)), with the possibility to specify the type of sum of squares (1, 2 or 3), the types of variables (Fixed or Random) and their relationships (crossed or nested) with the sole function of the package (FullyParamANOVA()). The resulting outputs are the same as in SAS software. A dataset (Butterfly) to test the function is also joined.

Acquire hourly meteorological data from stations located all over the world. There is a wealth of data available, with historic weather data accessible from nearly 30,000 stations. The available data is automatically downloaded from a data repository and processed into a tibble for the exact range of years requested. A relative humidity approximation is provided using the August-Roche-Magnus formula, which was adapted from Alduchov and Eskridge (1996) <doi:10.1175%2F1520-0450%281996%29035%3C0601%3AIMFAOS%3E2.0.CO%3B2>.

This package implements multiple allocation and selection strategies of sampling to construct core collections primarily from clustered or grouped germplasm collection data. Provides methods for allocating entries to clusters/groups based on group sizes, group-wise distance-based diversity metrics, and group-wise diversity index estimates. Includes procedures for selecting entries within clusters/groups through random sampling, genetic distance-based approaches, and optimized diversity metricâ based selection methods. See the package documentation for more, including full list of references for the methods implemented.

An implementation of split-population duration regression models. Unlike regular duration models, split-population duration models are mixture models that accommodate the presence of a sub-population that is not at risk for failure, e.g. cancer patients who have been cured by treatment. This package implements Weibull and Loglogistic forms for the duration component, and focuses on data with time-varying covariates. These models were originally formulated in Boag (1949) and Berkson and Gage (1952), and extended in Schmidt and Witte (1989).