Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.

Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.

Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) <DOI:10.1007/s00180-022-01299-0>.

Figures rendered on graphics devices are usually rescaled to fit pre-determined device dimensions. plotscale implements the reverse: desired plot dimensions are specified and device dimensions are calculated to accommodate marginal material, giving consistent proportions for plot elements. Default methods support grid graphics such as lattice and ggplot. See "example('devsize')" and "vignette('plotscale')".

This package provides functions which facilitate harmonization of data from multiple different datasets. Data harmonization involves taking data sources with differing values, creating coding instructions to create a harmonized set of values, then making those data modifications. psHarmonize will assist with data modification once the harmonization instructions are written. Coding instructions are written by the user to create a "harmonization sheet". This sheet catalogs variable names, domains (e.g. clinical, behavioral, outcomes), provides R code instructions for mapping or conversion of data, specifies the variable name in the harmonized data set, and tracks notes. The package will then harmonize the source datasets according to the harmonization sheet to create a harmonized dataset. Once harmonization is finished, the package also has functions that will create descriptive statistics using RMarkdown'. Data Harmonization guidelines have been described by Fortier I, Raina P, Van den Heuvel ER, et al. (2017) <doi:10.1093/ije/dyw075>. Additional details of our R package have been described by Stephen JJ, Carolan P, Krefman AE, et al. (2024) <doi:10.1016/j.patter.2024.101003>.

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.

Providing functions to diagnose and make inferences from various linear models, such as those obtained from aov', lm', glm', gls', lme', lmer', glmmTMB and semireg'. Inferences include predicted means and standard errors, contrasts, multiple comparisons, permutation tests, adjusted R-square and graphs.

Load the Just Another Gibbs Sampling (JAGS) module pexm'. The module provides the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling) implemented via JAGS. Details about the module implementation can be found in Mayrink et al. (2021) <doi:10.18637/jss.v100.i08>.

Helps you determine the analysis window to use when analyzing densely-sampled time-series data, such as EEG data, using permutation testing (Maris & Oostenveld, 2007) <doi:10.1016/j.jneumeth.2007.03.024>. These permutation tests can help identify the timepoints where significance of an effect begins and ends, and the results can be plotted in various types of heatmap for reporting. Mixed-effects models are supported using an implementation of the approach by Lee & Braun (2012) <doi:10.1111/j.1541-0420.2011.01675.x>.

This package provides tools to import, clean, and visualize movement data, particularly from motion capture systems such as Optitrack's Motive', the Straw Lab's Flydra', or from other sources. We provide functions to remove artifacts, standardize tunnel position and tunnel axes, select a region of interest, isolate specific trajectories, fill gaps in trajectory data, and calculate 3D and per-axis velocity. For experiments of visual guidance, we also provide functions that use subject position to estimate perception of visual stimuli.

In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.

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>.

Data and analysis from an experiment with improving touch typing speed, using the tDCS PlatoWork headset produced by PlatoScience.

This package provides data set and functions for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).

Latent group structures are a common challenge in panel data analysis. Disregarding group-level heterogeneity can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty. This package addresses the issue of unobservable group structures by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) <doi:10.1016/j.jeconom.2022.12.002>. PAGFL identifies latent group structures and group-specific coefficients in a single step. On top of that, we extend the PAGFL to time-varying coefficient functions (FUSE-TIME), following Haimerl et al. (2025) <doi:10.48550/arXiv.2503.23165>.

This uses a mixed integer mathematical programming (MIP) approach for building and solving multi-action planning problems, where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for spatial aspects. Thus, optimizing the connectivity and conservation effectiveness of the prioritized units and of the deployed actions. The package is capable of handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the gurobi installation vignette of the prioritizr package (available in <https://prioritizr.net/articles/gurobi_installation_guide.html>). Instead, CPLEX optimization solver can be obtain from IBM CPLEX web page (available here <https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>). Additionally, the rcbc R package (available at <https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>). Methods used in the package refers to Salgado-Rojas et al. (2020) <doi:10.1016/j.ecolmodel.2019.108901>, Beyer et al. (2016) <doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015) <doi:10.1371/journal.pone.0128027> and Watts et al. (2009) <doi:10.1016/j.envsoft.2009.06.005>. See the prioriactions website for more information, documentations and examples.

This package provides a fast and flexible framework for agglomerative partitioning. partition uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. partition is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. partition is based on the Partition framework discussed in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>.

This package provides functions for constructing dashboards for business process monitoring. Building on the event log objects class from package bupaR'. Allows the use to assemble custom shiny dashboards based on process data.

The permubiome R package was created to perform a permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in a pairwise manner, after a random shuffling of data into the different groups of study with a prior selection of the microbiome features with the largest variation among groups. Previous to the permutation test itself, data can be normalized according to different methods proposed to handle microbiome data ('proportions or Anders'). The median-based differences between groups resulting from the multiple simulations are fitted to a normal distribution with the aim to calculate their significance. A multiple testing correction based on Benjamini-Hochberg method (fdr) is finally applied to extract the differentially presented features between groups of your dataset. LATEST UPDATES: v1.1 and olders incorporates function to parse COLUMN format; v1.2 and olders incorporates -optimize- function to maximize evaluation of features with largest inter-class variation; v1.3 and olders includes the -size.effect- function to perform estimation statistics using the bootstrap-coupled approach implemented in the dabestr (>=0.3.0) R package. Current v1.3.2 fixed bug with "Class" recognition and updated dabestr functions.

The use of overparameterization is proposed with combinatorial analysis to test a broader spectrum of possible ARIMA models. In the selection of ARIMA models, the most traditional methods such as correlograms or others, do not usually cover many alternatives to define the number of coefficients to be estimated in the model, which represents an estimation method that is not the best. The popstudy package contains several tools for statistical analysis in demography and time series based in Shryock research (Shryock et. al. (1980) <https://books.google.co.cr/books?id=8Oo6AQAAMAAJ>).

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".

In gene sequencing methods, the topological features of protein-protein interaction (PPI) networks are often used, such as ToppNet <https://toppgene.cchmc.org>. In this study, a candidate gene prioritization method was proposed for non-communicable diseases considering disease risks transferred between genes in weighted disease PPI networks with weights for nodes and edges based on functional information.

This package provides functions and datasets to support Valliant, Dever, and Kreuter (2018), <doi:10.1007/978-3-319-93632-1>, "Practical Tools for Designing and Weighting Survey Samples". Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs, and single-stage audit sample designs. Functions are included that will group geographic units accounting for distances apart and measures of size. Other functions compute variance components for multistage designs, sample sizes in two-phase designs, and a stopping rule for ending data collection. A number of example data sets are included.

In short, this package is a locator for cool, refreshing beverages. It will find and return the nearest location where you can get a cold one.