Statistical tools for analyzing time-to-event data using machine learning. Implements survival stacking for conditional survival estimation, standardized survival function estimation for current status data, and methods for algorithm-agnostic variable importance. See Wolock CJ, Gilbert PB, Simon N, and Carone M (2024) <doi:10.1080/10618600.2024.2304070>.

This package provides influence function-based methods to evaluate a longitudinal surrogate marker in a censored time-to-event outcome setting, with plug-in and targeted maximum likelihood estimation options. Details are described in: Agniel D and Parast L (2025). "Robust Evaluation of Longitudinal Surrogate Markers with Censored Data." Journal of the Royal Statistical Society: Series B <doi:10.1093/jrsssb/qkae119>. A tutorial for this package can be found at <https://www.laylaparast.com/survivalsurrogate> and a Shiny App implementing the package can be found at <https://parastlab.shinyapps.io/survivalsurrogateApp/>.

Fast, lightweight toolkit for data splitting. Data sets can be partitioned into disjoint groups (e.g. into training, validation, and test) or into (repeated) k-folds for subsequent cross-validation. Besides basic splits, the package supports stratified, grouped as well as blocked splitting. Furthermore, cross-validation folds for time series data can be created. See e.g. Hastie et al. (2001) <doi:10.1007/978-0-387-84858-7> for the basic background on data partitioning and cross-validation.

This package provides a step-down procedure for controlling the False Discovery Proportion (FDP) in a competition-based setup, implementing Dong et al. (2020) <arXiv:2011.11939>. Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression.

This package provides a user-friendly wrapper for web automation, using either chromote or selenium'. Provides a simple and consistent API to make web scraping and testing scripts easy to write and understand. Elements are lazy, and automatically wait for the website to be valid, resulting in reliable and reproducible code, with no visible impact on the experience of the programmer.

This package provides a set of plotting methods for simmer trajectories and simulations.

Draw syntenic relationships between genome assemblies. There are 3 functions which take a tab delimited file containing alignment data for syntenic blocks between genomes to produce either a linear alignment plot, an evolution highway style plot, or a painted ideogram representing syntenic relationships. There is also a function to convert alignment data in the DESCHRAMBLER/inferCAR format to the required data structure.

This package implements the Self-Similarity Test for Normality (SSTN), a new statistical test designed to assess whether a given sample originates from a normal distribution. The procedure is based on iteratively estimating the characteristic function of the sum of standardized i.i.d. random variables and comparing it to the characteristic function of the standard normal distribution. A Monte Carlo procedure is used to determine the empirical distribution of the test statistic under the null hypothesis. Details of the methodology are described in Anarat and Schwender (2025), "A normality test based on self-similarity" (Submitted).

During the preparation of data set(s) one usually performs some sanity checks. The idea is that irrespective of where the checks are performed, they are centralized by this package in order to list all at once with examples if a check failed.

Efficient variational inference methods for fully Bayesian Gaussian Process Regression (GPR) models with hierarchical shrinkage priors, including the triple gamma prior for effective variable selection and covariance shrinkage in high-dimensional settings. The package leverages normalizing flows to approximate complex posterior distributions. For details on implementation, see Knaus (2025) <doi:10.48550/arXiv.2501.13173>.

Implement different Item Response Theory (IRT) based procedures for the development of static short test forms (STFs) from a test. Two main procedures are considered (Epifania, Anselmi & Robusto, 2022 <doi:10.1007/978-3-031-27781-8_7>). The procedures differ in how the most informative items are selected for the inclusion in the STF, either by considering their item information functions without any reference to any specific latent trait level (benchmark procedure) or by considering their information with respect to specific latent trait levels, denoted as theta targets (theta target procedure). Three methods are implemented for the definition of the theta targets: (i) as the midpoints of equal intervals on the latent trait, (ii) as the centroids of the clusters obtained by clustering the latent trait, and (iii) as user-defined values. Importantly, the number of theta targets defines the number of items included in the STF. For further details on the procedure, please refer to Epifania, Anselmi & Robusto (2022) <doi:10.1007/978-3-031-27781-8_7>.

This package provides a robust and powerful empirical Bayesian approach is developed for replicability analysis of two large-scale experimental studies. The method controls the false discovery rate by using the joint local false discovery rate based on the replicability null as the test statistic. An EM algorithm combined with a shape constraint nonparametric method is used to estimate unknown parameters and functions. [Li, Y. et al., (2024), <doi:10.1371/journal.pgen.1011423>].

This package provides fitting functions and other tools for decision confidence and metacognition researchers, including meta-d'/d', often considered to be the gold standard to measure metacognitive efficiency, and information-theoretic measures of metacognition. Also allows to fit and compare several static models of decision making and confidence.

Extended Susceptible-Exposed-Infected-Recovery Model for handling high false negative rate and symptom based administration of diagnostic tests. <doi:10.1101/2020.09.24.20200238>.

This package provides a set of tools for estimating hierarchical linear models and effect sizes based on data from single-case designs. Functions are provided for calculating standardized mean difference effect sizes that are directly comparable to standardized mean differences estimated from between-subjects randomized experiments, as described in Hedges, Pustejovsky, and Shadish (2012) <DOI:10.1002/jrsm.1052>; Hedges, Pustejovsky, and Shadish (2013) <DOI:10.1002/jrsm.1086>; Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>; and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) <DOI:10.1016/j.jsp.2023.02.002>. Includes an interactive web interface.

This package provides digital tools for performing analyses within Social Dynamics and complexity in the Ancient Mediterranean (SDAM), which is a research group based at the Department of History and Classical Studies at Aarhus University.

This package creates a contextual menu that can be triggered with keyboard shortcuts or programmatically. This can replace traditional sidebars or navigation bars, thereby enhancing the user experience with lighter user interfaces.

This package provides functions connecting to the Salesforce Platform APIs (REST, SOAP, Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards) <https://trailhead.salesforce.com/content/learn/modules/api_basics/api_basics_overview>. "API" is an acronym for "application programming interface". Most all calls from these APIs are supported as they use CSV, XML or JSON data that can be parsed into R data structures. For more details please see the Salesforce API documentation and this package's website <https://stevenmmortimer.github.io/salesforcer/> for more information, documentation, and examples.

This package provides functions to be used in conjunction with the Sequential package that allows for planning of observational database studies that will be analyzed with exact sequential analysis. This package supports Poisson- and binomial-based data. The primary function, seq_wrapper(...), accepts parameters for simulation of a simple exposure pattern and for the Sequential package setup and analysis functions. The exposure matrix is used to simulate the true and false positive and negative populations (Green (1983) <doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993) <doi:10.1093/oxfordjournals.aje.a116805>). Functions are then run from the Sequential package on these populations, which allows for the exploration of outcome misclassification in data.

This package provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the mapproj package.

Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.

This package provides functions to manipulate PDF files: fill out PDF forms; merge multiple PDF files into one; remove selected pages from a file; rename multiple files in a directory; rotate entire pdf document; rotate selected pages of a pdf file; Select pages from a file; splits single input PDF document into individual pages; splits single input PDF document into parts from given points.

This package provides a rich set of UI components for building Shiny applications, including inputs, containers, overlays, menus, and various utilities. All components from Fluent UI (the underlying JavaScript library) are available and have usage examples in R.

It implements parametric formulas of soil water retention or conductivity curve. At the moment, only Van Genuchten (for soil water retention curve) and Mualem (for hydraulic conductivity) were implemented. See reference (<http://en.wikipedia.org/wiki/Water_retention_curve>).