Compute relative or absolute population trends across space and time using predictions from models fitted to ecological population abundance data, as described in Knape (2025) <doi:10.1016/j.ecolind.2025.113435>. The package supports models fitted by mgcv or brms', and draws from posterior predictive distributions.

This package implements the Bayesian model selection method with suspected latent grouping factor methodology of Metzger and Franck (2020), <doi:10.1080/00401706.2020.1739561>. SLGF detects latent heteroscedasticity or group-based regression effects based on the levels of a user-specified categorical predictor.

An extension of animate.css that allows user to easily add animations to any UI element in shiny app using the elements id.

Load Avro Files into Apache Spark using sparklyr'. This allows to read files from Apache Avro <https://avro.apache.org/>.

Allows the user to connect with IBGE's (Instituto Brasileiro de Geografia e Estatistica, see <https://www.ibge.gov.br/> for more information) SIDRA API in a flexible way. SIDRA is the acronym to "Sistema IBGE de Recuperacao Automatica" and is the system where IBGE turns available aggregate data from their researches.

This package provides a simple interface to recursively list files from a directory, filter them using a regular expression, read their contents, and extract lines that match a user-defined pattern. The package returns a dataframe containing the matched lines, their line numbers, file paths, and the corresponding matched substrings. Designed for quick code base exploration, log inspection, or any use case involving pattern-based file and line filtering.

Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations.

Implementation of the SAM prior and generation of its operating characteristics for dynamically borrowing information from historical data. For details, please refer to Yang et al. (2023) <doi:10.1111/biom.13927>.

This package provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and OpenMP (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>. The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) <doi:10.1098/rsif.2008.0172> or the Particle Markov Chain Monte Carlo ('PMCMC') algorithm of Andrieu and others (2010) <doi:10.1111/j.1467-9868.2009.00736.x>.

This package provides a graphical user interface for cross-sectional network modeling with the statnet software suite <https://github.com/statnet>.

This package provides an automated routing engine for longitudinal missing data. It utilizes a Lagrange-constrained Random Forest based on sample size, missingness rate, and skew to preserve structural variance.

We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points.

Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. Sun Q., Li X., et al (2020) <doi:10.1038/s41467-019-14238-3>. In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure.

Split Knockoff is a data adaptive variable selection framework for controlling the (directional) false discovery rate (FDR) in structural sparsity, where variable selection on linear transformation of parameters is of concern. This proposed scheme relaxes the linear subspace constraint to its neighborhood, often known as variable splitting in optimization. Simulation experiments can be reproduced following the Vignette. Split Knockoffs is first defined in Cao et al. (2021) <doi:10.48550/arXiv.2103.16159>.

Get started with new projects by dropping a skeleton of a new project into a new or existing directory, initialise git repositories, and create reproducible environments with the renv package. The package allows for dynamically named files, folders, file content, as well as the functionality to drop individual template files into existing projects.

Convenient tools for exchanging files securely from within R. By encrypting the content safe passage of files (shipment) can be provided by common but insecure carriers such as ftp and email. Based on asymmetric cryptography no management of shared secrets is needed to make a secure shipment as long as authentic public keys are available. Public keys used for secure shipments may also be obtained from external providers as part of the overall process. Transportation of files will require that relevant services such as ftp and email servers are available.

Secure handling of API keys can be difficult. This package provides secure convenience functions for entering / handling API keys and opening connections via inversion of control on those keys. Works seamlessly between production and developer environments.

This package implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2025) <doi:10.48550/arXiv.2407.10846>, and allows for the statistical modeling of multi-group rank data in combination with object variables. The package also allows for the simulation of synthetic multi-group rank data.

Proposes an application for sequence prediction generalizing the similarity within the network of previous sequences.

Identify statistically significant flow clusters using the local spatial network autocorrelation statistic G_ij* proposed by Berglund and Karlström (1999) <doi:10.1007/s101090050013>. The metric, an extended statistic of Getis/Ord G ('Getis and Ord 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>, detects a group of flows having similar traits in terms of directionality. You provide OD data and the associated polygon to get results with several parameters, some of which are defined by spdep package.

Database of genes which frequently sustain somatic mutations, but are unlikely to drive cancer.

Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique. Includes support for binary, count, and continuous data. The first regression is a logistic regression, which uses a known ratio (the probability of being sampled given that the subject/observation was referred divided by the probability of being sampled given that the subject/observation was no referred) as an offset to estimate the probability of being referred given outcome and covariates. The second regression uses this estimated probability to calculate the mean population response given covariates.

This package provides a time input widget for Shiny. This widget allows intuitive time input in the [hh]:[mm]:[ss] or [hh]:[mm] (24H) format by using a separate numeric input for each time component. The interface with R uses date-time objects. See the project page for more information and examples.

An implementation of sensitivity analysis for phylogenetic comparative methods. The package is an umbrella of statistical and graphical methods that estimate and report different types of uncertainty in PCM: (i) Species Sampling uncertainty (sample size; influential species and clades). (ii) Phylogenetic uncertainty (different topologies and/or branch lengths). (iii) Data uncertainty (intraspecific variation and measurement error).