Fast exponentiation when the exponent is an integer.

Extracts growth, survival, and local neighborhood density information from repeated, fine-scale maps of organism occurrence. Further information about this package can be found in our journal article, "plantTracker: An R package to translate maps of plant occurrence into demographic data" published in 2022 in Methods in Ecology and Evolution (Stears, et al., 2022) <doi:10.1111/2041-210X.13950>.

Finds equivalence classes corresponding to a symmetric relation or undirected graph. Finds total order consistent with partial order or directed graph (so-called topological sort).

This package provides an interactive Shiny-based toolkit for conducting latent structure analyses, including Latent Profile Analysis (LPA), Latent Class Analysis (LCA), Latent Trait Analysis (LTA/IRT), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The implementation is grounded in established methodological frameworks: LPA is supported through tidyLPA (Rosenberg et al., 2018) <doi:10.21105/joss.00978>, LCA through poLCA (Linzer & Lewis, 2011) <doi:10.32614/CRAN.package.poLCA> & glca (Kim & Kim, 2024) <doi:10.32614/CRAN.package.glca>, LTA/IRT via mirt (Chalmers, 2012) <doi:10.18637/jss.v048.i06>, and EFA via psych (Revelle, 2025). SEM and CFA functionalities build upon the lavaan framework (Rosseel, 2012) <doi:10.18637/jss.v048.i02>. Users can upload datasets or use built-in examples, fit models, compare fit indices, visualize results, and export outputs without programming.

An R interface to pikchr (<https://pikchr.org>, pronounced "picture"), a PIC'-like markup language for creating diagrams within technical documentation. Originally developed by Brian Kernighan, PIC has been adapted into pikchr by D. Richard Hipp, the creator of SQLite'. pikchr is designed to be embedded in fenced code blocks of Markdown or other documentation markup languages, making it ideal for generating diagrams in text-based formats. This package allows R users to seamlessly integrate the descriptive syntax of pikchr for diagram creation directly within the R environment.

This package provides a polycross is the pollination by natural hybridization of a group of genotypes, generally selected, grown in isolation from other compatible genotypes in such a way to promote random open pollination. A particular practical application of the polycross method occurs in the production of a synthetic variety resulting from cross-pollinated plants. Laying out these experiments in appropriate designs, known as polycross designs, would not only save experimental resources but also gather more information from the experiment. Different experimental situations may arise in polycross nurseries which may be requiring different polycross designs (Varghese et. al. (2015) <doi:10.1080/02664763.2015.1043860>. " Experimental designs for open pollination in polycross trials"). This package contains a function named PD() which generates nine types of polycross designs suitable for various experimental situations.

Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".

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'.

Makes the time series prediction easier by automatizing this process using four main functions: prep(), modl(), pred() and postp(). Features different preprocessing methods to homogenize variance and to remove trend and seasonality. Also has the potential to bring together different predictive models to make comparatives. Features ARIMA and Data Mining Regression models (using caret).

Estimates when and where a model-guided treatment strategy may outperform a treat-all or treat-none approach by Monte Carlo simulation and evaluation of the Net Monetary Benefit. Details can be viewed in Parsons et al. (2023) <doi:10.21105/joss.05328>.

This package provides tools for fitting periodic coefficients regression models to data where periodicity plays a crucial role. It allows users to model and analyze relationships between variables that exhibit cyclical or seasonal patterns, offering functions for estimating parameters and testing the periodicity of coefficients in linear regression models. For simple periodic coefficient regression model see Regui et al. (2024) <doi:10.1080/03610918.2024.2314662>.

It enables sparklyr to integrate with Spark Connect', and Databricks Connect by providing a wrapper over the PySpark python library.

Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.

Text mining of PubMed Abstracts (text and XML) from <https://pubmed.ncbi.nlm.nih.gov/>.

This package provides a shiny application for visualizing high-dimensional data using non-linear dimensionality reduction (NLDR) techniques such as t-SNE and UMAP. It provides an interactive platform to explore high-dimensional datasets, diagnose the quality of the embeddings using the quollr package, and compare different NLDR methods.

This package provides functions to automatically build a directory structure for a new R project. Using this structure, ProjectTemplate automates data loading, preprocessing, library importing and unit testing.

It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.

This package performs demographic, bifurcation and evolutionary analysis of physiologically structured population models, which is a class of models that consistently translates continuous-time models of individual life history to the population level. A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) <doi:10.1142/S0218202501001264>. O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) <doi:10.1016/S0040-5809(02)00058-8>. A.M. de Roos (2008) <doi:10.1111/j.1461-0248.2007.01121.x>.

Calculation of the parametric, nonparametric confidence intervals for the difference or ratio of location parameters, nonparametric confidence interval for the Behrens-Fisher problem and for the difference, ratio and odds-ratio of binomial proportions for comparison of independent samples. Common wrapper functions to split data sets and apply confidence intervals or tests to these subsets. A by-statement allows calculation of CI separately for the levels of further factors. CI are not adjusted for multiplicity.

Presentation of a new goodness-of-fit normality test based on the Lilliefors method. For details on this method see: Sulewski (2019) <doi:10.1080/03610918.2019.1664580>.

This wrapper houses PathLit API endpoints for R. The usage of these endpoints require the use of an API key which can be obtained at <https://www.pathlit.io/docs/cli/>.

This package provides a set of tools to implement the non-parametric bounds and Bayesian methods for assessing post-treatment bias developed in Blackwell, Brown, Hill, Imai, and Yamamoto (2025) <doi:10.1017/pan.2025.3>.

Price comparisons within or between countries provide an overall measure of the relative difference in prices, often denoted as price levels. This package provides index number methods for such price comparisons (e.g., The World Bank, 2011, <doi:10.1596/978-0-8213-9728-2>). Moreover, it contains functions for sampling and characterizing price data.

This package provides tools for exploring projection pursuit classification tree using various projection pursuit indexes.