This package contains modeling and analytical tools for plant ecophysiology. MODELING: Simulate C3 photosynthesis using the Farquhar, von Caemmerer, Berry (1980) <doi:10.1007/BF00386231> model as described in Buckley and Diaz-Espejo (2015) <doi:10.1111/pce.12459>. It uses units to ensure that parameters are properly specified and transformed before calculations. Temperature response functions get automatically "baked" into all parameters based on leaf temperature following Bernacchi et al. (2002) <doi:10.1104/pp.008250>. The package includes boundary layer, cuticular, stomatal, and mesophyll conductances to CO2, which each can vary on the upper and lower portions of the leaf. Use straightforward functions to simulate photosynthesis over environmental gradients such as Photosynthetic Photon Flux Density (PPFD) and leaf temperature, or over trait gradients such as CO2 conductance or photochemistry. ANALYTICAL TOOLS: Fit ACi (Farquhar et al. (1980) <doi:10.1007/BF00386231>) and AQ curves (Marshall & Biscoe (1980) <doi:10.1093/jxb/31.1.29>), temperature responses (Heskel et al. (2016) <doi:10.1073/pnas.1520282113>; Kruse et al. (2008) <doi:10.1111/j.1365-3040.2008.01809.x>, Medlyn et al. (2002) <doi:10.1046/j.1365-3040.2002.00891.x>, Hobbs et al. (2013) <doi:10.1021/cb4005029>), respiration in the light (Kok (1956) <doi:10.1016/0006-3002(56)90003-8>, Walker & Ort (2015) <doi:10.1111/pce.12562>, Yin et al. (2009) <doi:10.1111/j.1365-3040.2009.01934.x>, Yin et al. (2011) <doi:10.1093/jxb/err038>), mesophyll conductance (Harley et al. (1992) <doi:10.1104/pp.98.4.1429>), pressure-volume curves (Koide et al. (2000) <doi:10.1007/978-94-009-2221-1_9>, Sack et al. (2003) <doi:10.1046/j.0016-8025.2003.01058.x>, Tyree et al. (1972) <doi:10.1093/jxb/23.1.267>), hydraulic vulnerability curves (Ogle et al. (2009) <doi:10.1111/j.1469-8137.2008.02760.x>, Pammenter et al. (1998) <doi:10.1093/treephys/18.8-9.589>), and tools for running sensitivity analyses particularly for variables with uncertainty (e.g. g_mc(), gamma_star(), R_d()).

This package provides functions for solving systems of delay differential equations by interfacing with numerical routines written by Simon N. Wood, including contributions from Benjamin J. Cairns. These numerical routines first appeared in Simon Wood's solv95 program. This package includes a vignette and a complete user's guide. PBSddesolve originally appeared on CRAN under the name ddesolve'. That version is no longer supported. The current name emphasizes a close association with other PBS packages, particularly PBSmodelling'.

Data and examples from meta-analyses in psychology research.

Utility functions produced specifically for (but not limited to) working with ProjectTemplate data pipelines. This package helps to quickly create and manage sequentially numbered scripts, quickly set up logging with log4r and functions to help debug and monitor procedures.

This package provides a simple function to bind a piped object to a placeholder symbol to enable complex function evaluation with the base R |> pipe.

Application of the Partitioning-Around-Medoids (PAM) clustering algorithm described in Schubert, E. and Rousseeuw, P.J.: "Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms." Information Systems, vol. 101, p. 101804, (2021). <doi:10.1016/j.is.2021.101804>. It uses a binary format for storing and retrieval of matrices developed for the jmatrix package but the functionality of jmatrix is included here, so you do not need to install it. Also, it is used by package scellpam', so if you have installed it, you do not need to install this package. PAM can be applied to sets of data whose dissimilarity matrix can be very big. It has been tested with up to 100.000 points. It does this with the help of the code developed for other package, jmatrix', which allows the matrix not to be loaded in R memory (which would force it to be of double type) but it gets from disk, which allows using float (or even smaller data types). Moreover, the dissimilarity matrix is calculated in parallel if the computer has several cores so it can open many threads. The initial part of the PAM algorithm can be done with the BUILD or LAB algorithms; the BUILD algorithm has been implemented in parallel. The optimization phase implements the FastPAM1 algorithm, also in parallel. Finally, calculation of silhouette is available and also implemented in parallel.

Numerical integration of continuously differentiable functions f(x,y) over simple closed polygonal domains. The following cubature methods are implemented: product Gauss cubature (Sommariva and Vianello, 2007, <doi:10.1007/s10543-007-0131-2>), the simple two-dimensional midpoint rule (wrapping spatstat.geom functions), and adaptive cubature for radially symmetric functions via line integrate() along the polygon boundary (Meyer and Held, 2014, <doi:10.1214/14-AOAS743>, Supplement B). For simple integration along the axes, the cubature package is more appropriate.

Computes power and level tables for goodness-of-fit tests for the normal, Laplace, and uniform distributions. Generates output in LaTeX format to facilitate reporting and reproducibility. Explanatory graphs help visualize the statistical power of test statistics under various alternatives. For more details, see Lafaye De Micheaux and Tran (2016) <doi:10.18637/jss.v069.i03>.

This package provides functions for landscape analysis and data retrieval. The package allows users to download environmental variables from global datasets (e.g., WorldClim, ESA WorldCover, Nighttime Lights), and to compute spatial and landscape metrics using a hexagonal grid system based on the H3 spatial index. It is useful for ecological modeling, biodiversity studies, and spatial data processing in landscape ecology. Fick and Hijmans (2017) <doi:10.1002/joc.5086>. Zanaga et al. (2022) <doi:10.5281/zenodo.7254221>. Uber Technologies Inc. (2022) "H3: Hexagonal hierarchical spatial index".

An R-Shiny application implementing a method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits <doi:10.1002/ajpa.23855>.

Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the MaxQuant software suite (from .txt files) or mzTab files (ideally from OpenMS QualityControl tool). Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format. Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015) <doi:10.1021/acs.jproteome.5b00780>.

Spatial estimation of a prevalence surface or a relative risks surface, using data from a Demographic and Health Survey (DHS) or an analog survey, see Larmarange et al. (2011) <doi:10.4000/cybergeo.24606>.

This package provides functions tailored for scientific and student communities involved in plant science research. Functionalities encompass estimation chlorophyll content according to Arnon (1949) <doi:10.1104/pp.24.1.1>, determination water potential of Polyethylene glycol(PEG)6000 as in Michel and Kaufmann (1973) <doi:10.1104/pp.51.5.914> and functions related to estimation of yield related indices like Abiotic tolerance index as given by Moosavi et al.(2008)<doi:10.22059/JDESERT.2008.27115>, Geometric mean productivity (GMP) by Fernandez (1992) <ISBN:92-9058-081-X>, Golden Mean by Moradi et al.(2012)<doi:10.14207/ejsd.2012.v1n3p543>, HAM by Schneider et al.(1997)<doi:10.2135/cropsci1997.0011183X003700010007x>,MPI and TOL by Hossain etal., (1990)<doi:10.2135/cropsci1990.0011183X003000030030x>, RDI by Fischer et al. (1979)<doi:10.1071/AR9791001>,SSI by Fisher et al.(1978)<doi:10.1071/AR9780897>, STI by Fernandez (1993)<doi:10.22001/wvc.72511>,YSI by Bouslama & Schapaugh (1984)<doi:10.2135/cropsci1984.0011183X002400050026x>, Yield index by Gavuzzi et al.(1997)<doi:10.4141/P96-130>.

Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) <doi:10.1007/BF02310791> and Harshman (1970) <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.

Routines for flexible functional form estimation via basis regression, with model selection via the adaptive LASSO or SCAD to prevent overfitting.

We extend two general methods of moment estimators to panel vector autoregression models (PVAR) with p lags of endogenous variables, predetermined and strictly exogenous variables. This general PVAR model contains the first difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also provide specification tests (Hansen overidentification test, lag selection criterion and stability test of the PVAR polynomial) and classical structural analysis for PVAR models such as orthogonal and generalized impulse response functions, bootstrapped confidence intervals for impulse response analysis and forecast error variance decompositions.

An R implementation of methods employed in the field of pedometrics, soil science discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil using statistical and computational methods. The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/>.

Implementation of Probabilistic Regression Trees (PRTree), providing functions for model fitting and prediction, with specific adaptations to handle missing values. The main computations are implemented in Fortran for high efficiency. The package is based on the PRTree methodology described in Alkhoury et al. (2020), "Smooth and Consistent Probabilistic Regression Trees" <https://proceedings.neurips.cc/paper_files/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf>. Details on the treatment of missing data and implementation aspects are presented in Prass, T.S.; Neimaier, A.S.; Pumi, G. (2025), "Handling Missing Data in Probabilistic Regression Trees: Methods and Implementation in R" <doi:10.48550/arXiv.2510.03634>.

Considering the singly imputed synthetic data generated via plug-in sampling under the multivariate normal model, draws inference procedures including the generalized variance, the sphericity test, the test for independence between two subsets of variables, and the test for the regression of one set of variables on the other. For more details see Klein et al. (2021) <doi:10.1007/s13571-019-00215-9>.

This package provides functions to compute the potential model as defined by Stewart (1941) <doi:10.1126/science.93.2404.89>. Several options are available to customize the model, such as the possibility to fine-tune the distance friction functions or to use custom distance matrices. Some computations are parallelized to improve their efficiency.

The aim of postpack is to provide the infrastructure for a standardized workflow for mcmc.list objects. These objects can be used to store output from models fitted with Bayesian inference using JAGS', WinBUGS', OpenBUGS', NIMBLE', Stan', or even custom MCMC algorithms. Although the coda R package provides some methods for these objects, it is somewhat limited in easily performing post-processing tasks for specific nodes. Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time for a particular question or figure. Thus, many postpack functions support performing tasks on a subset of nodes, where the subset is specified with regular expressions. The functions in postpack streamline the extraction, summarization, and diagnostics of specific monitored nodes after model fitting. Further, because there is rarely only ever one model under consideration, postpack scales efficiently to perform the same tasks on output from multiple models simultaneously, facilitating rapid assessment of model sensitivity to changes in assumptions.

We present Platypus', an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. Platypus provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology. This R version of Platypus is part of the ePlatypus ecosystem for computational analysis of immunogenomics data: Yermanos et al. (2021) <doi:10.1093/nargab/lqab023>, Cotet et al. (2023) <doi:10.1093/bioinformatics/btad553>.

Compute standard Non-Compartmental Analysis (NCA) parameters for typical pharmacokinetic analyses and summarize them.

This package provides a collection of R functions that are widely used by the Petersen Lab. Included are functions for various purposes, including evaluating the accuracy of judgments and predictions, performing scoring of assessments, generating correlation matrices, conversion of data between various types, data management, psychometric evaluation, extensions related to latent variable modeling, various plotting capabilities, and other miscellaneous useful functions. By making the package available, we hope to make our methods reproducible and replicable by others and to help others perform their data processing and analysis methods more easily and efficiently. The codebase is provided in Petersen (2025) <doi:10.5281/zenodo.7602890> and on CRAN': <doi: 10.32614/CRAN.package.petersenlab>. The package is described in "Principles of Psychological Assessment: With Applied Examples in R" (Petersen, 2024, 2025a) <doi:10.1201/9781003357421>, <doi:10.25820/work.007199>, <doi:10.5281/zenodo.6466589> and in "Fantasy Football Analytics: Statistics, Prediction, and Empiricism Using R" (Petersen, 2025b).