This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. Consider the case of reproducing a time series data set of size 20 that uses an autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1. When one checks the arima.sin() function's estimated parameters, it's possible that after a single trial or a few more, one won't find the precise parameters. This enables one to look for the ideal RNG setting for a simulation that will accurately duplicate the desired parameters.

Hybrid control design is a way to borrow information from external controls to augment concurrent controls in a randomized controlled trial and is expected to overcome the feasibility issue when adequate randomized controlled trials cannot be conducted. A major challenge in the hybrid control design is its inability to eliminate a prior-data conflict caused by systematic imbalances in measured or unmeasured confounding factors between patients in the concurrent treatment/control group and external controls. To prevent the prior-data conflict, a combined use of propensity score matching and Bayesian commensurate prior has been proposed in the context of hybrid control design. The propensity score matching is first performed to guarantee the balance in baseline characteristics, and then the Bayesian commensurate prior is constructed while discounting the information based on the similarity in outcomes between the concurrent and external controls. psBayesborrow is a package to implement the propensity score matching and the Bayesian analysis with commensurate prior, as well as to conduct a simulation study to assess operating characteristics of the hybrid control design, where users can choose design parameters in flexible and straightforward ways depending on their own application.

This package implements the methodology of Huling, Smith, and Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part modeling framework to handle semi-continuous data by separately modeling the positive part of the outcome and an indicator of whether each outcome is positive, but still results in a single treatment rule. High dimensional data is handled with a cooperative lasso penalty, which encourages the coefficients in the two models to have the same sign.

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>.

Bland (2009) <doi:10.1136/bmj.b3985> recommended to base study sizes on the width of the confidence interval rather the power of a statistical test. The goal of presize is to provide functions for such precision based sample size calculations. For a given sample size, the functions will return the precision (width of the confidence interval), and vice versa.

This package provides an interface to the GenderAPI.io Phone Number Validation & Formatter API (<https://www.genderapi.io>) for validating international phone numbers, detecting number type (mobile, landline, Voice over Internet Protocol (VoIP)), retrieving region and country metadata, and formatting numbers to E.164 or national format. Designed to simplify integration into R workflows for data validation, Customer Relationship Management (CRM) data cleaning, and analytics tasks. Full documentation is available at <https://www.genderapi.io/docs-phone-validation-formatter-api>.

It provides utility functions for investigating changes within R packages. The pkgInfo() function extracts package information such as exported and non-exported functions as well as their arguments. The pkgDiff() function compares this information for two versions of a package and creates a diff file viewable in a browser.

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

This package provides tools for exchanging pedigree data between the pedsuite packages and the Familias software for forensic kinship computations (Egeland et al. (2000) <doi:10.1016/s0379-0738(00)00147-x>). These functions were split out from the forrel package to streamline maintenance and provide a lightweight alternative for packages otherwise independent of forrel'.

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.

This package provides access to material from the book "Processing and Analyzing Financial Data with R" by Marcelo Perlin (2017) available at <https://sites.google.com/view/pafdr/home>.

Access the Public Transport Victoria Timetable API <https://www.ptv.vic.gov.au/footer/data-and-reporting/datasets/ptv-timetable-api/>, with results returned as familiar R data structures. Retrieve information on stops, routes, disruptions, departures, and more.

Create a project directory structure, along with typical files for that project. This allows projects to be quickly and easily created, as well as for them to be standardized. Designed specifically with scientists in mind (mainly bio-medical researchers, but likely applies to other fields).

Farmer, J., D. Jacobs (2108) <DOI:10.1371/journal.pone.0196937>. A multivariate nonparametric density estimator based on the maximum-entropy method. Accurately predicts a probability density function (PDF) for random data using a novel iterative scoring function to determine the best fit without overfitting to the sample.

It provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.

Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist. These difficulties are related to those arising from logistic and multinomial regression. References for methods are given in the separate function documents. Supported by grant NSF DMS 1712839.

Many datasets and a set of graphics (based on ggplot2), statistics, effect sizes and hypothesis tests are provided for analysing paired data with S4 class.

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>.

Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes. Mrode, R. A. (2005) <ISBN:9780851989969, 0851989969>; Nilforooshan, M.A. (2022) <doi:10.37496/rbz5120210131>.

Utility functions for the handling, analysis and visualisation of data from portable emissions measurement systems ('PEMS') and other similar mobile activity monitoring devices. The package includes a dedicated pems data class that manages many of the quality control, unit handling and data archiving issues that can hinder efforts to standardise PEMS research.

It is often advantageous to test a hypothesis more than once in the context of propensity score analysis (Rosenbaum, 2012) <doi:10.1093/biomet/ass032>. The functions in this package facilitate bootstrapping for propensity score analysis (PSA). By default, bootstrapping using two classification tree methods (using rpart and ctree functions), two matching methods (using Matching and MatchIt packages), and stratification with logistic regression. A framework is described for users to implement additional propensity score methods. Visualizations are emphasized for diagnosing balance; exploring the correlation relationships between bootstrap samples and methods; and to summarize results.

Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) <doi:10.48550/arXiv.2504.00423>.

Computation of predictive information criteria (PIC) from select model object classes for model selection in predictive contexts. In contrast to the more widely used Akaike Information Criterion (AIC), which are derived under the assumption that target(s) of prediction (i.e. validation data) are independently and identically distributed to the fitting data, the PIC are derived under less restrictive assumptions and thus generalize AIC to the more practically relevant case of training/validation data heterogeneity. The methodology featured in this package is based on Flores (2021) <https://iro.uiowa.edu/esploro/outputs/doctoral/A-new-class-of-information-criteria/9984097169902771?institution=01IOWA_INST> "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity".

This package provides tools for downloading, reading and analyzing the Continuous National Household Sample Survey - PNADC, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package survey'.