This package provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. This package was designed to generate data for the simulations performed in Pesigan, Russell, and Chow (2025) <doi:10.1037/met0000779>.

Efficiently estimates treatment effects in settings with randomized staggered rollouts, using tools proposed by Roth and Sant'Anna (2023) <doi:10.48550/arXiv.2102.01291>.

English is the native language for only 5% of the World population. Also, only 17% of us can understand this text. Moreover, the Latin alphabet is the main one for merely 36% of the total. The early computer era, now a very long time ago, was dominated by the US. Due to the proliferation of the internet, smartphones, social media, and other technologies and communication platforms, this is no longer the case. This package replaces base R string functions (such as grep(), tolower(), sprintf(), and strptime()) with ones that fully support the Unicode standards related to natural language and date-time processing. It also fixes some long-standing inconsistencies, and introduces some new, useful features. Thanks to ICU (International Components for Unicode) and stringi', they are fast, reliable, and portable across different platforms.

This package provides tools for manipulating sound files for bioacoustic analysis, and preparing analyses these for publication. The package validates that values are physically possible wherever feasible.

Create sampling designs using the surface reconstruction algorithm. Original method by: Olsson, D. 2002. A method to optimize soil sampling from ancillary data. Poster presenterad at: NJF seminar no. 336, Implementation of Precision Farming in Practical Agriculture, 10-12 June 2002, Skara, Sweden.

This package provides functions for creating, displaying, and evaluating stopping rules for safety monitoring in clinical studies.

This package contains various functions to be used for simulation education, including simple Monte Carlo simulation functions, queueing simulation functions, variate generation functions capable of producing independent streams and antithetic variates, functions for illustrating random variate generation for various discrete and continuous distributions, and functions to compute time-persistent statistics. Also contains functions for visualizing: event-driven details of a single-server queue model; a Lehmer random number generator; variate generation via acceptance-rejection; and of generating a non-homogeneous Poisson process via thinning. Also contains two queueing data sets (one fabricated, one real-world) to facilitate input modeling. More details on the use of these functions can be found in Lawson and Leemis (2015) <doi:10.1109/WSC.2017.8248124>, in Kudlay, Lawson, and Leemis (2020) <doi:10.1109/WSC48552.2020.9384010>, and in Lawson and Leemis (2021) <doi:10.1109/WSC52266.2021.9715299>.

Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers. Includes implementations of methods described in Mayrhofer and Filzmoser (2023) <doi:10.1016/j.ecosta.2023.04.003>.

An htmlwidget of the human body that allows you to hide/show and assign colors to 79 different body parts. The human widget is an htmlwidget', so it works in Quarto documents, R Markdown documents, or any other HTML medium. It also functions as an input/output widget in a shiny app.

This package provides a fast and adaptable tool to convert photos and images into usable colour schemes for data visualisation. Contains functionality to extract colour palettes from images, as well for the conversion of images between colour spaces.

The estimation method proposed by Chen and Yi (2021) <doi:10.1111/biom.13331> is extended to the analysis of survival data, accommodating commonly used survival models while accounting for measurement error and network structures among covariates.

This package provides a general purpose simulation-based power analysis API for routine and customized simulation experimental designs. The package focuses exclusively on Monte Carlo simulation experiment variants of (expected) prospective power analyses, criterion analyses, compromise analyses, sensitivity analyses, and a priori/post-hoc analyses. The default simulation experiment functions defined within the package provide stochastic variants of the power analysis subroutines in G*Power 3.1 (Faul, Erdfelder, Buchner, and Lang, 2009) <doi:10.3758/brm.41.4.1149>, along with various other parametric and non-parametric power analysis applications (e.g., mediation analyses) and support for Bayesian power analysis by way of Bayes factors or posterior probability evaluations. Additional functions for building empirical power curves, reanalyzing simulation information, and for increasing the precision of the resulting power estimates are also included, each of which utilize similar API structures. For further details see the associated publication in Chalmers (2025) <doi:10.3758/s13428-025-02787-z>.

The StockDistFit package provides functions for fitting probability distributions to stock price data. The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution. References are as follows: Siew et al. (2008) <https://www.jstage.jst.go.jp/article/jappstat/37/1/37_1_1/_pdf/-char/ja> and Benth et al. (2008) <https://books.google.co.ke/books?hl=en&lr=&id=MHNpDQAAQBAJ&oi=fnd&pg=PR7&dq=Stochastic+modeling+of+commodity+prices+using+the+Variance+Gamma+(VG)+model.+&ots=YNIL2QmEYg&sig=XZtGU0lp4oqXHVyPZ-O8x5i7N3w&redir_esc=y#v=onepage&q&f=false>.

Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing with data from complex sample surveys are available too.

Succinctly and correctly format statistical summaries of various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). This package also includes empirical tests, such as Monte Carlo and bootstrap distribution estimates.

Easy to use interfaces to a number of imputation methods that fit in the not-a-pipe operator of the magrittr package.

Non-parametric test, originally proposed by Stute (1997) <https://www.jstor.org/stable/2242560>, that the expectation of a dependent variable Y given an independent variable D is linear in D.

This package provides a group of functions that support the sf package, focused primarily on repairing polygons that break when re-projected.

An extension to the individual claim simulator called SynthETIC (on CRAN), to simulate the evolution of case estimates of incurred losses through the lifetime of an insurance claim. The transactional simulation output now comprises key dates, and both claim payments and revisions of estimated incurred losses. An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator" <arXiv:2109.04058>.

This package implements stacked elastic net regression (Rauschenberger 2021 <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005 <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).

Users may specify what fundamental qualities of a new study have or have not changed in an attempt to reproduce or replicate an original study. A comparison of the differences is visualized. Visualization approach follows Patil', Peng', and Leek (2016) <doi:10.1101/066803>.

This package provides SAS'-style IF/ELSE chains, independent IF rules, and DELETE logic for data.table', enabling clinical programmers to express Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM)-style derivations in familiar SAS-like syntax. Methods are informed by clinical data standards described in CDISC SDTM and ADaM implementation guides. See <https://www.cdisc.org/standards/foundational/sdtm> and <https://www.cdisc.org/standards/foundational/adam>.

Easily create alerts, notifications, modals, info tips and loading screens in Shiny'. Includes several options to customize alerts and notifications by including text, icons, images and buttons. When wrapped around a Shiny output, loading screen is automatically displayed while the output is being recalculated.

This package provides a novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. SpaTopic is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see <https://xiyupeng.github.io/SpaTopic/>.