Automated pain scoring from paw withdrawal tracking data. Based on Jones et al. (2020) "A machine-vision approach for automated pain measurement at millisecond timescales" <doi:10.7554/eLife.57258>.

Algorithms to implement various Bayesian penalized survival regression models including: semiparametric proportional hazards models with lasso priors (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and three other shrinkage and group priors (Lee et al., Stat Anal Data Min, 2015 <doi:10.1002/sam.11266>); parametric accelerated failure time models with group/ordinary lasso prior (Lee et al. Comput Stat Data Anal, 2017 <doi:10.1016/j.csda.2017.02.014>).

Fit a time-series model to a crop phenology data, such as time-series rice canopy height. This package returns the model parameters as the summary statistics of crop phenology, and these parameters will be useful to characterize the growth pattern of each cultivar and predict manually-measured traits, such as days to heading and biomass. Please see Taniguchi et al. (2022) <doi:10.3389/fpls.2022.998803> and Taniguchi et al. (2025) <doi: 10.3389/frai.2024.1477637> for detail. This package has been designed for scientific use. Use for commercial purposes shall not be allowed.

Implementations of Power Fuzzy Clustering (PFC) and Power Fuzzy Cluster-wise Regression (PFCR) for multivariate data. The package supports Minkowski distances, with the L1 case solved via iteratively re-weighted least squares and the case p > 1 solved via coordinate-wise root finding, as well as an adaptive, regularised Mahalanobis distance with per-cluster covariance matrices. Both plain fuzzy clustering and cluster-wise linear regression are provided. The corresponding paper can be found at Nguyen P.T., Tortora C., and Punzo A. (2026) <doi:10.1109/TFUZZ.2026.3683998>.

Recursive construction of balanced incomplete block designs (BIBDs), their successive generations, resolvable BIBDs (RBIBDs) and associated uniform designs (UDs), derived from finite projective geometries PG(m, p) over a Galois field GF(p) of any prime order p. Implements and generalises the method of Boudraa, Gheribi-Aoulmi and Laib (2013, International Journal of Research and Reviews in Applied Sciences, 17(2), 167-176), which was previously available only for p = 2, and the uniform design constructions of Fang et al. (2004) <doi:10.1016/S0012-365X(03)00100-6>. Designs of every recursion stage can be extracted, and all constructions are validated against the parameters published in the original paper.

An implementation of two functions that estimate values for percentiles from an ordered categorical variable as described by Reardon (2011, isbn:978-0-87154-372-1). One function estimates percentile differences from two percentiles while the other returns the values for every percentile from 1 to 100.

This package contains functions to obtain the operational characteristics of bioequivalence studies in Two-Stage Designs (TSD) via simulations.

This package provides an updated database of accepted endemic plant taxa from Peru. The current collection contains over 8,000 taxonomic records at species and infraspecific ranks. Data are derived from Govaerts, R., Nic Lughadha, E., Black, N. et al., The World Checklist of Vascular Plants: A continuously updated resource for exploring global plant diversity', published in Sci Data 8, 215 (2021) <doi:10.1038/s41597-021-00997-6>.

Binding models which are useful when analysing protein-ligand interactions by techniques such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance (SPR). Naman B. Shah, Thomas M. Duncan (2014) <doi:10.3791/51383>. Hoang H. Nguyen et al. (2015) <doi:10.3390/s150510481>. After initial binding parameters are known, binding curves can be simulated and parameters can be varied. The models within this package may also be used to fit a curve to measured binding data using non-linear regression.

The Penn World Table 10.x (<https://www.rug.nl/ggdc/productivity/pwt/>) provides information on relative levels of income, output, input, and productivity for 183 countries between 1950 and 2019.

This package provides tools for Bayesian power analysis and assurance calculations using the statistical frameworks of brms and INLA'. Includes simulation-based approaches, support for multiple decision rules (direction, threshold, ROPE), sequential designs, and visualisation helpers. Methods are based on Kruschke (2014, ISBN:9780124058880) "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan", O'Hagan & Stevens (2001) <doi:10.1177/0272989X0102100307> "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness", Kruschke (2018) <doi:10.1177/2515245918771304> "Rejecting or Accepting Parameter Values in Bayesian Estimation", Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x> "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", and Bürkner (2017) <doi:10.18637/jss.v080.i01> "brms: An R Package for Bayesian Multilevel Models using Stan".

Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts).

Detection of hotspots of point events on a linear network as proposed by MrkviÄ ka et al. (2025) <doi:10.2139/ssrn.5337003> using the R package GET', see Myllymäki and MrkviÄ ka (2024) <doi:10.18637/jss.v111.i03>.

This package provides some easy-to-use functions for spatial analyses of (plant-) phenological data sets and satellite observations of vegetation.

ProTracker is a popular music tracker to sequence music on a Commodore Amiga machine. This package offers the opportunity to import, export, manipulate and play ProTracker module files. Even though the file format could be considered archaic, it still remains popular to this date. This package intends to contribute to this popularity and therewith keeping the legacy of ProTracker and the Commodore Amiga alive.

Generate nicely formatted HTML tables to display estimation results for pharmacometric models.

Draw 2 dimensional and three dimensional plot for multiple regression models using package ggplot2 and rgl'. Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess).

Estimation and inference of spatial and spatio-temporal semiparametric models including spatial or spatio-temporal non-parametric trends, parametric and non-parametric covariates and, possibly, a spatial lag for the dependent variable and temporal correlation in the noise. The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>; Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.

Reconstruction of paleoclimate niches using phylogenetic comparative methods and projection reconstructed niches onto paleoclimate maps. The user can specify various models of trait evolution or estimate the best fit model, include fossils, use one or multiple phylogenies for inference, and make animations of shifting suitable habitat through time. This model was first used in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and Rivera et al (2020). Lawing and Polly (2011) <doi:10.1371/journal.pone.0028554> "Pleistocene climate, phylogeny and climate envelope models: An integrative approach to better understand species response to climate change" Lawing et al (2016) <doi:10.1086/687202> "Including fossils in phylogenetic climate reconstructions: A deep time perspective on the climatic niche evolution and diversification of spiny lizards (Sceloporus)" Rivera et al (2020) <doi:10.1111/jbi.13915> "Reconstructing historical shifts in suitable habitat of Sceloporus lineages using phylogenetic niche modelling.".

This package provides functions and mined database from UniProt focusing on post-translational modifications to do single enrichment analysis (SEA) and protein set enrichment analysis (PSEA). Payman Nickchi, Uladzislau Vadadokhau, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2025) <doi:10.1002/pmic.202400238>.

This package provides a system to increase the efficiency of dynamic web-scraping with RSelenium by leveraging parallel processing. You provide a function wrapper for your RSelenium scraping routine with a set of inputs, and parsel runs it in several browser instances. Chunked input processing as well as error catching and logging ensures seamless execution and minimal data loss, even when unforeseen RSelenium errors occur. You can additionally build safe scraping functions with minimal coding by utilizing constructor functions that act as wrappers around RSelenium methods.

This package provides functions to measure Alpha, Beta and Gamma Proximity to Irreplaceability. The methods for Alpha and Beta irreplaceability were first described in: Baisero D., Schuster R. & Plumptre A.J. Redefining and Mapping Global Irreplaceability. Conservation Biology 2021;1-11. <doi:10.1111/cobi.13806>.

Support for a variety of commonly used precision agriculture operations. Includes functions to download and process raw satellite images from Sentinel-2 <https://documentation.dataspace.copernicus.eu/APIs/OData.html>. Includes functions that download vegetation index statistics for a given period of time, without the need to download the raw images <https://documentation.dataspace.copernicus.eu/APIs/SentinelHub/Statistical.html>. There are also functions to download and visualize weather data in a historical context. Lastly, the package also contains functions to process yield monitor data. These functions can build polygons around recorded data points, evaluate the overlap between polygons, clean yield data, and smooth yield maps.

This package provides software to facilitate the design, testing, and operation of computer models. It focuses particularly on tools that make it easy to construct and edit a customized graphical user interface ('GUI'). Although our simplified GUI language depends heavily on the R interface to the Tcl/Tk package, a user does not need to know Tcl/Tk'. Examples illustrate models built with other R packages, including PBSmapping', PBSddesolve', and BRugs'. A complete user's guide PBSmodelling-UG.pdf shows how to use this package effectively.