An extension of the AlphaSimR package (<https://cran.r-project.org/package=AlphaSimR>) for stochastic simulations of honeybee populations and breeding programmes. SIMplyBee enables simulation of individual bees that form a colony, which includes a queen, fathers (drones the queen mated with), virgin queens, workers, and drones. Multiple colony can be merged into a population of colonies, such as an apiary or a whole country of colonies. Functions enable operations on castes, colony, or colonies, to ease R scripting of whole populations. All AlphaSimR functionality with respect to genomes and genetic and phenotype values is available and further extended for honeybees, including haplo-diploidy, complementary sex determiner locus, colony events (swarming, supersedure, etc.), and colony phenotype values.

An efficient sensitivity analysis for stochastic models based on Monte Carlo samples. Provides weights on simulated scenarios from a stochastic model, such that stressed random variables fulfil given probabilistic constraints (e.g. specified values for risk measures), under the new scenario weights. Scenario weights are selected by constrained minimisation of the relative entropy to the baseline model. The SWIM package is based on Pesenti S.M., Millossovich P., Tsanakas A. (2019) "Reverse Sensitivity Testing: What does it take to break the model" <openaccess.city.ac.uk/id/eprint/18896/> and Pesenti S.M. (2021) "Reverse Sensitivity Analysis for Risk Modelling" <https://www.ssrn.com/abstract=3878879>.

This package performs simulation and inference of diffusion processes on circle. Stochastic correlation models based on circular diffusion models are provided. For details see Majumdar, S. and Laha, A.K. (2024) "Diffusion on the circle and a stochastic correlation model" <doi:10.48550/arXiv.2412.06343>.

Explore continuous, date and categorical variables. sumvar aims to bring the ease and simplicity of the "sum" and "tab" functions from stata'.

Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012) <doi:10.1111/j.1466-8238.2011.00655.x>.

It provides easy internationalization of Shiny applications. It can be used as standalone translation package to translate reports, interactive visualizations or graphical elements as well.

This package provides tools for conditional and spatially dependent density estimation using Spatial Logistic Gaussian Processes (SLGPs). The approach represents probability densities through finite-rank Gaussian process priors transformed via a spatial logistic density transformation, enabling flexible non-parametric modeling of heterogeneous data. Functionality includes density prediction, quantile and moment estimation, sampling methods, and preprocessing routines for basis functions. Applications arise in spatial statistics, machine learning, and uncertainty quantification. The methodology builds on the framework of Leonard (1978) <doi:10.1111/j.2517-6161.1978.tb01655.x>, Lenk (1988) <doi:10.1080/01621459.1988.10478625>, Tokdar (2007) <doi:10.1198/106186007X210206>, Tokdar (2010) <doi:10.1214/10-BA605>, and is further aligned with recent developments in Bayesian non-parametric modelling: see Gautier (2023) <https://boristheses.unibe.ch/4377/>, and Gautier (2025) <doi:10.48550/arXiv.2110.02876>).

Access statistical information on welfare and health in Finland from the Sotkanet open data portal <https://sotkanet.fi/sotkanet/fi/index>.

This package provides functions to design and apply tests that are anytime valid. The functions can be used to design hypothesis tests in the prospective/randomised control trial setting or in the observational/retrospective setting. The resulting tests remain valid under both optional stopping and optional continuation. The current version includes safe t-tests and safe tests of two proportions. For details on the theory of safe tests, see Grunwald, de Heide and Koolen (2019) "Safe Testing" <arXiv:1906.07801>, for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald (2020) "The Safe Logrank Test: Error Control under Continuous Monitoring with Unlimited Horizon" <arXiv:2011.06931v3> and Turner, Ly and Grunwald (2021) "Safe Tests and Always-Valid Confidence Intervals for contingency tables and beyond" <arXiv:2106.02693> for details on safe contingency table tests.

This package provides a collection of statistical hypothesis tests of functional time series. While it will include more tests when the related literature are enriched, this package contains the following key tests: functional stationarity test, functional trend stationarity test, functional unit root test, to name a few.

This package provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion. Please contact the package maintainer with any publications making use of this package in an effort to maintain a repository of dietary selections studies.

Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. This enables parallelization of the rendering, separating the data source and visualization location and to provide web services.

This package provides functions to manipulate PDF files: fill out PDF forms; merge multiple PDF files into one; remove selected pages from a file; rename multiple files in a directory; rotate entire pdf document; rotate selected pages of a pdf file; Select pages from a file; splits single input PDF document into individual pages; splits single input PDF document into parts from given points.

Detection of anomalous space-time clusters using the scan statistics methodology. Focuses on prospective surveillance of data streams, scanning for clusters with ongoing anomalies. Hypothesis testing is made possible by Monte Carlo simulation. Allévius (2018) <doi:10.21105/joss.00515>.

Obtain parameters of Svensson's Method, including percentage agreement, systematic change and individual change. Also, the contingency table can be generated. Svensson's Method is a rank-invariant nonparametric method for the analysis of ordered scales which measures the level of change both from systematic and individual aspects. For the details, please refer to Svensson E. Analysis of systematic and random differences between paired ordinal categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.

Design a Bayesian seamless multi-arm biomarker-enriched phase II/III design with the survival endpoint with allowing sample size re-estimation. James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) <doi:10.1038/bjc.2015.278>. Guosheng Yin, Nan Chen, J. Jack Lee (2018) <doi:10.1007/s12561-017-9199-7>. Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) <doi:10.1002/sim.6971>.

An introduction to a couple of novel predictive variable selection methods for generalised boosted regression modeling (gbm). They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the steprf package, and also based on predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.

Sequence detector in this package contains a specific automaton model that can be used to learn and detect data and process sequences. Automaton model in this package is capable of learning and tracing sequences. Automaton model can be found in Krleža, Vrdoljak, BrÄ iÄ (2019) <doi:10.1109/ACCESS.2019.2955245>. This research has been partly supported under Competitiveness and Cohesion Operational Programme from the European Regional and Development Fund, as part of the Integrated Anti-Fraud System project no. KK.01.2.1.01.0041. This research has also been partly supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009.

Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

This package provides a tool for working with SQLite databases. SQLite has some idiosyncrasies and limitations that impose some hurdles to the R developer who is using this database as a repository. For instance, SQLite doesn't have a date type and sqliteutils has some functions to deal with that.

Many packages use htmlwidgets <https://CRAN.R-project.org/package=htmlwidgets> for interactive plotting of spatial data. This package provides functions for converting R objects, such as simple features, into structures suitable for use in htmlwidgets mapping libraries.

Model age schedules of mortality, nqx, suitable for a life table. This package implements the SVD-Comp mortality model indexed by either child or child/adult mortality. Given input value(s) of either 5q0 or (5q0, 45q15), the qx() function generates single-year 1qx or 5-year 5qx conditional age-specific probabilities of dying. See Clark (2016) <doi:10.48550/arXiv.1612.01408> and Clark (2019) <doi:10.1007/s13524-019-00785-3>.

From output files obtained from the software ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.

Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. <doi:10.2307/259247> Hester, P.T., & Adams, K.M. (2013) <doi:10.1016/j.procs.2013.09.282>.