Pirat enables the imputation of missing values (either MNARs or MCARs) in bottom-up LC-MS/MS proteomics data using a penalized maximum likelihood strategy. It does not require any parameter tuning, it models the instrument censorship from the data available. It accounts for sibling peptides correlations and it can leverage complementary transcriptomics measurements.

Competence-based knowledge space theory (CbKST) is an extension of knowledge space theory (KST) modeling the latent skills and competencies underlying the observable response behaviour as described by Heller & Stefanutti (2024) <doi:10.1142/9789811280481_0001>. The package focuses on the mappings between competence and performance level (skill (multi) map, problem function etc.).

Simulation tool to estimate the rate of success that surveys possessing user-specific characteristics have in identifying archaeological sites (or any groups of clouds of objects), given specific parameters of survey area, survey methods, and site properties. The survey approach used is largely based on the work of Kintigh (1988) <doi:10.2307/281113>.

Produce maximum likelihood estimates of common accuracy statistics for multiple measurement methods when a gold standard is not available. An R implementation of the expectation maximization algorithms described in Zhou et al. (2011) <doi:10.1002/9780470906514> with additional functions for creating simulated data and visualizing results. Supports binary, ordinal, and continuous measurement methods.

Create fake datasets that can be used for prototyping and teaching. This package provides a set of functions to generate fake data for a variety of data types, such as dates, addresses, and names. It can be used for prototyping (notably in shiny') or as a tool to teach data manipulation and data visualization.

This package provides a wrapper for the python module FIORA as well as a shiny'-App to facilitate data processing and visualization. FIORA allows to predict Mass-Spectra based on the SMILES code of chemical compounds. It is described in the Nature Communications article by Nowatzky (2025) <doi:10.1038/s41467-025-57422-4>.

We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) <doi:10.1093/bioinformatics/btaa017>.

This package provides a GUI interface for automating data extraction from multiple images containing scatter and bar plots, semi-automated tools to tinker with extraction attempts, and a fully-loaded point-and-click manual extractor with image zoom, calibrator, and classifier. Also provides detailed and R-independent extraction reports as fully-embedded .html records.

This package implements the LS-PLS (least squares - partial least squares) method described in for instance Jørgensen, K., Segtnan, V. H., Thyholt, K., Næs, T. (2004) "A Comparison of Methods for Analysing Regression Models with Both Spectral and Designed Variables" Journal of Chemometrics, 18(10), 451--464, <doi:10.1002/cem.890>.

This package provides a variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.

Classify missing data as missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). This step is required before handling missing data (e.g. mean imputation) so that bias is not introduced. See Little (1988) <doi:10.1080/01621459.1988.10478722> for the statistical rationale for the methods used.

Post-selection inference in linear regression models, constructing simultaneous confidence intervals across a user-specified universe of models. Implements the methodology described in Kuchibhotla, Kolassa, and Kuffner (2022) "Post-Selection Inference" <doi:10.1146/annurev-statistics-100421-044639> to ensure valid inference after model selection, with applications in high-dimensional settings like Lasso selection.

Quantile regression (QR) for Linear Mixed-Effects Models via the asymmetric Laplace distribution (ALD). It uses the Stochastic Approximation of the EM (SAEM) algorithm for deriving exact maximum likelihood estimates and full inference results for the fixed-effects and variance components. It also provides graphical summaries for assessing the algorithm convergence and fitting results.

This package implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering. The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and Kulldorff (1997) <doi:10.1080/03610929708831995>.

This package implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) <doi:10.48550/arXiv.2506.19988>. Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses.

The best way to implement middle ware for shiny Applications. tower is designed to make implementing behavior on top of shiny easy with a layering model for incoming HTTP requests and server sessions. tower is a very minimal package with little overhead, it is mainly meant for other package developers to implement new behavior.

Find, inspect and download statistical tables from the Romanian National Institute of Statistics TEMPO Online database. Tables can be listed and searched in Romanian or English, their structure inspected before downloading, and whole tables or filtered selections retrieved either as data frames or as CSV files that are kept up to date between sessions.

This package provides a modern, tested client for version 3 of The Movie Database ('TMDB') API. It modernises the interface originally provided by the legacy TMDb package by Capozio (2020) <https://CRAN.R-project.org/package=TMDb> while adding secure web requests, bearer-token authentication, structured errors, retries, safe query encoding, and automatic pagination.

Computes inequality measures of a given variable taking into account weights. Suitable for ratio, interval and ordered scale. Includes measures developed by Gini, Theil, Leti, Palma ratio, 20:20 ratio, Allison and Foster, Jenkins, Cowell and Flechaire, Abul Naga and Yalcin, Apouey, Blair and Lacy. Bootstrap provides distribution of inequality measures enabling significance tests.

Estimation methods for zero-inflated Poisson factor analysis (ZIPFA) on sparse data. It provides estimates of coefficients in a new type of zero-inflated regression. It provides a cross-validation method to determine the potential rank of the data in the ZIPFA and conducts zero-inflated Poisson factor analysis based on the determined rank.

Researchers across disciplines often face biased regression model estimates due to endogenous regressors correlated with the error term. Traditional solutions require instrumental variables (IVs), which are often difficult to find and validate. This package provides flexible, alternative IV-free methods using copulas, as described in the practical guide to endogeneity correction using copulas (Yi Qian, Tony Koschmann, and Hui Xie 2025) <doi:10.1177/00222429251410844>. The current version implements the two-stage copula endogeneity correction (2sCOPE) method to fit models with continuous endogenous regressors and both continuous and discrete exogenous regressors, as described in Fan Yang, Yi Qian, and Hui Xie (2024) <doi:10.1177/00222437241296453>. Using this method, users can address regressor endogeneity problems in nonexperimental data without requiring IVs.

This R package is for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis, such as differential gene expression or transcript usage.

This package provides methods for spatial data analysis, especially raster data. The included methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction. Processing of very large files is supported.

This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization.