Permute treatment labels for taxa and environmental gradients to generate an empirical distribution of change points. This is an extension for the TITAN2 package <https://cran.r-project.org/package=TITAN2>.

This package provides a central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations. In contrast to the classic power-transformations, the methods in this package allows for theoretical driven user input and the possibility to compare with a non-parametric transformation.

Run Queries against the API of Piwik Pro <https://developers.piwik.pro/en/latest/custom_reports/http_api/http_api.html>. The result is a tibble.

This package provides a set of functions designed to calculate the standardised precipitation and standardised precipitation evapotranspiration indices using NASA POWER data as described in Blain et al. (2023) <doi:10.2139/ssrn.4442843>. These indices are calculated using a reference data source. The functions verify if the indices estimates meet the assumption of normality and how well NASA POWER estimates represent real-world data. Indices are calculated in a routine mode. Potential evapotranspiration amounts and the difference between rainfall and potential evapotranspiration are also calculated. The functions adopt a basic time scale that splits each month into four periods. Days 1 to 7, days 8 to 14, days 15 to 21, and days 22 to 28, 29, 30, or 31, where TS=4 corresponds to a 1-month length moving window (calculated 4 times per month) and TS=48 corresponds to a 12-month length moving window (calculated 4 times per month).

The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The PSIM package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) <doi:10.1016/j.matdes.2009.11.020>.

This package provides a function PWI() that calculates prize winner indices based on bibliometric data is provided. The default is the Derek de Solla Price Memorial Medal'. Users can provide recipients of other prizes.

This package provides a collection of tools for approximating the PDQ functions (respectively, the cumulative distribution, density, and quantile) of probability distributions via classical expansions involving moments and cumulants.

Easy and efficient access to the API provided by Prevedere', an industry insights and predictive analytics company. Query and download indicators, models and workbenches built with Prevedere for further analysis and reporting <https://www.prevedere.com/>.

Test-based Image structural similarity measure and test of independence. This package implements the key functions of two tasks: (1) computing image structural similarity measure PSSIM of Wang, Maldonado and Silwal (2011) <DOI:10.1016/j.csda.2011.04.021>; and (2) test of independence between a response and a covariate in presence of heteroscedastic treatment effects proposed by Wang, Tolos, and Wang (2010) <DOI:10.1002/cjs.10068>.

This package provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly. It is built upon ggplot2 and other plotting packages, and is designed to be easy to use and to work seamlessly with ggplot2 objects. It is particularly useful for creating complex plots with multiple layers, facets, and annotations. It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees. The package is designed to be flexible and customizable, and to work well with the ggplot2 ecosystem. The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.

There are three sets of functions. The first produces basic properties of a graph and generates samples from multinomial distributions to facilitate the simulation functions (they maybe used for other purposes as well). The second provides various simulation functions for a Potts model in Potts, R. B. (1952) <doi:10.1017/S0305004100027419>. The third currently includes only one function which computes the normalizing constant of a Potts model based on simulation results.

Particle swarm optimization - a basic variant.

Allows the user to convert PDF tables to formats more amenable to analysis ('.csv', .xml', or .xlsx') by wrapping the PDFTables API. In order to use the package, the user needs to sign up for an API account on the PDFTables website (<https://pdftables.com/pdf-to-excel-api>). The package works by taking a PDF file as input, uploading it to PDFTables, and returning a file with the extracted data.

Directly pipes raw quantitative PCR (qPCR) machine outputs into downstream analyses using the comparative Ct (Delta-Delta Ct) method described by Livak and Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Streamlines the workflow from Excel export to publication-ready plots. Integrates unique visual quality control by reconstructing 96-well plate heatmaps, allowing users to instantly detect pipetting errors, edge effects, and outliers. Key features include automated error propagation, laboratory master mix calculations, and generation of bar charts and volcano plots.

This package provides a native R client library for querying the Prometheus time-series database, using the PromQL query language.

Check a data frame for personal information, including names, location, disability status, and geo-coordinates.

Reverse depends for a given package are queued such that multiple workers can run the reverse-dependency tests in parallel.

Validate data in data frames, tibble objects, Spark DataFrames', and database tables. Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. User-defined thresholds for failure rates allow for the determination of appropriate reporting actions. Many other workflows are available including an information management workflow, where the aim is to record, collect, and generate useful information on data tables.

Computes the Patient-Reported Outcomes (PROs) Joint Contrast (PJC), a residual-based summary that captures information left over after accounting for the clinical Disease Activity index for Psoriatic Arthritis (cDAPSA). PROs (pain and patient global assessment) and joint counts (swollen and tender) are standardized, then each component is adjusted for standardized cDAPSA using natural spline coefficients that were derived from previously published models. The resulting residuals are standardized and combined using fixed principal component loadings, to yield a continuous PJC score and quartile groupings. This package provides a calculator for applying those published coefficients to new datasets; it does not itself estimate spline models or principal components.

This package creates a data frame with the residuals of partial regressions of the main explanatory variable and the variable of interest. This method follows the Frisch-Waugh-Lovell theorem, as explained in Lovell (2008) <doi:10.3200/JECE.39.1.88-91>.

This package provides permutation methods for testing in high-dimensional linear models. The tests are often robust against heteroscedasticity and non-normality and usually perform well under anti-sparsity. See Hemerik, Thoresen and Finos (2021) <doi:10.1080/00949655.2020.1836183>.

This is a data only package, that provides distances from a paper plane experiment.

This package provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) <doi:10.1086/676622> to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984) <doi:10.1007/BF00275224>, bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices. Further details can be found in Torices & Muñoz-Pajares <doi:10.3732/apps.1400104>.

Miscellaneous utilities for parallelizing large computations. Alternative to MapReduce. File splitting and distributed operations such as sort and aggregate. "Software Alchemy" method for parallelizing most statistical methods, presented in N. Matloff, Parallel Computation for Data Science, Chapman and Hall, 2015. Includes a debugging aid.