Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components (de Jong S. & Kiers H. A. L. (1992) <doi:10.1016/0169-7439(92)80100-I>). Several rotation and model selection options are provided.

Miscellaneous printing of numeric or statistical results in R Markdown or Quarto documents according to guidelines of the "Publication Manual" of the American Psychological Association (2020, ISBN: 978-1-4338-3215-4). These guidelines are usually referred to as APA style (<https://apastyle.apa.org/>) and include specific rules on the formatting of numbers and statistical test results. APA style has to be implemented when submitting scientific reports in a wide range of research fields, especially in the social sciences. The default output of numbers in the R console or R Markdown and Quarto documents does not meet the APA style requirements, and reformatting results manually can be cumbersome and error-prone. This package covers the automatic conversion of R objects to textual representations that meet the APA style requirements, which can be included in R Markdown or Quarto documents. It covers some basic statistical tests (t-test, ANOVA, correlation, chi-squared test, Wilcoxon test) as well as some basic number printing manipulations (formatting p-values, removing leading zeros for numbers that cannot be greater than one, and others). Other packages exist for formatting numbers and tests according to the APA style guidelines, such as papaja (<https://cran.r-project.org/package=papaja>) and apa (<https://cran.r-project.org/package=apa>), but they do not offer all convenience functionality included in prmisc'. The vignette has an overview of most of the functions included in the package.

Routines for two different test types, the Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are provided (Kurz and Spanhel (2022) <doi:10.1214/22-EJS2051>). The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The CCC test comes with a decision tree approach to allow testing in high-dimensional settings.

Calculate Plant Stress Response Index (PSRI) from time-series germination data with optional radicle vigor integration. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) <doi:10.1002/agg2.20087>. Provides clean, direct PSRI calculations suitable for agricultural research and statistical analysis. Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach.

This package provides functions for fitting abundance distributions over environmental gradients to the species in ecological communities, and tools for simulating the fossil assemblages from those abundance models for such communities, as well as simulating assemblages across various patterns of sedimentary history and sampling. These tools are for particular use with fossil records with detailed age models and abundance distributions used for calculating environmental gradients from ordinations or other indices based on fossil assemblages.

This package performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior distributions either the power-expected-posterior (PEP) or the intrinsic (a special case of the former) (Fouskakis and Ntzoufras (2022) <doi: 10.1214/21-BA1288>, Fouskakis and Ntzoufras (2020) <doi: 10.3390/econometrics8020017>). The prior distribution on model space is the uniform over all models or the uniform on model dimension (a special case of the beta-binomial prior). The selection is performed by either implementing a full enumeration and evaluation of all possible models or using the Markov Chain Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) <doi: 10.2307/1403615>). Complementary functions for hypothesis testing, estimation and predictions under Bayesian model averaging, as well as, plotting and printing the results are also provided. The results can be compared to the ones obtained under other well-known priors on model parameters and model spaces.

Statistical functions to describe a Pareto Positive Stable (PPS) distribution and fit it to real data. Graphical and statistical tools to validate the fits are included.

Plots with high flexibility and easy handling, including informative regression diagnostics for many models.

An implementation of an S3 class based on a double vector for storing and displaying precision teaching measures, representing a growing or a decaying (multiplicative) change between two frequencies. The main format method allows researchers to display measures (including data.frame) that respect the established conventions in the precision teaching community (i.e., prefixed multiplication or division symbol, displayed number <= 1). Basic multiplication and division methods are allowed and other useful functions are provided for creating, converting or inverting precision teaching measures. For more details, see Pennypacker, Gutierrez and Lindsley (2003, ISBN: 1-881317-13-7).

This package contains common univariate and multivariate portmanteau test statistics for time series models. These tests are based on using asymptotic distributions such as chi-square distribution and based on using the Monte Carlo significance tests. Also, it can be used to simulate from univariate and multivariate seasonal time series models.

Visualize the partitions of simple decision trees, involving one or two predictors, on the scale of the original data. Provides an intuitive alternative to traditional tree diagrams, by visualizing how a decision tree divides the predictor space in a simple 2D plot alongside the original data. The parttree package supports both classification and regression trees from rpart and partykit', as well as trees produced by popular frontend systems like tidymodels and mlr3'. Visualization methods are provided for both base R graphics and ggplot2'.

Set of functions for analysis of Principal Coordinates of Phylogenetic Structure (PCPS).

Computes the generalized synthetic control estimator described in Powell (2017) <doi:10.7249/WR1142>. Provides both point estimates, and hypothesis testing.

Static code analyses for R packages using the external code-tagging libraries ctags and gtags'. Static analyses enable packages to be analysed very quickly, generally a couple of seconds at most. The package also provides access to a database generating by applying the main function to the full CRAN archive, enabling the statistical properties of any package to be compared with all other CRAN packages.

This package provides functions for easily reading and processing binary data files created by Pamguard (<https://www.pamguard.org/>). All functions for directly reading the binary data files are based on MATLAB code written by Michael Oswald.

This package provides a collection of functions to simulate, estimate and forecast a wide range of regression based dynamic models for positive time series. This package implements the results presented in Prass, T.S.; Pumi, G.; Taufemback, C.G. and Carlos, J.H. (2025). "Positive time series regression models: theoretical and computational aspects". Computational Statistics 40, 1185â 1215. <doi:10.1007/s00180-024-01531-z>.

Enables the manufacturing, analysis and display of pressure volume curves. From the progression of the curves, turgor loss point, osmotic potential and apoplastic fraction can be derived. Methods adapted from Bartlett, Scoffoni and Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x>.

Simulates judgments of frequency and duration based on the Probability Associator Time (PASS-T) model. PASS-T is a memory model based on a simple competitive artificial neural network. It can imitate human judgments of frequency and duration, which have been extensively studied in cognitive psychology (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010) <https://psycnet.apa.org/record/2010-18204-003>). The PASS-T model is an extension of the PASS model (Sedlmeier, 2002, ISBN:0198508638). The package provides an easy way to run simulations, which can then be compared with empirical data in human judgments of frequency and duration.

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".

This utility eases the debugging of literate documents ('noweb files) by patching the synchronization information (the .synctex(.gz) file) produced by pdflatex with concordance information produced by Sweave or knitr and Sweave or knitr ; this allows for bilateral communication between a text editor (visualizing the noweb source) and a viewer (visualizing the resultant PDF'), thus bypassing the intermediate TeX file.

Compute and visualize package download counts and percentile ranks from Posit/RStudio's CRAN mirror.

The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) <doi:10.1093/jamia/ocx111>.

Calculation of Predictive Moran's eigenvector maps (pMEM), as defined by Guénard and Legendre (In Press) "Spatially-explicit predictions using spatial eigenvector maps" <doi:10.5281/zenodo.13356457>. Methods in Ecology and Evolution. This method enables scientists to predict the values of spatially-structured environmental variables. Multiple types of pMEM are defined, each one implemented on the basis of spatial weighting function taking a range parameter, and sometimes also a shape parameter. The code's modular nature enables programers to implement new pMEM by defining new spatial weighting functions.

Calculate parametric mortality and Fertility models, following packages BaSTA in Colchero, Jones and Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and BaFTA <https://github.com/fercol/BaFTA>, summary statistics (e.g. ageing rates, life expectancy, lifespan equality, etc.), life table and product limit estimators from census data.