Like similar profiling tools, the proffer package automatically detects sources of slowness in R code. The distinguishing feature of proffer is its utilization of pprof', which supplies interactive visualizations that are efficient and easy to interpret. Behind the scenes, the profile package converts native Rprof() data to a protocol buffer that pprof understands. For the documentation of proffer', visit <https://r-prof.github.io/proffer/>. To learn about the implementations and methodologies of pprof', profile', and protocol buffers, visit <https://github.com/google/pprof>. <https://protobuf.dev>, and <https://github.com/r-prof/profile>, respectively.

This package implements the estimator of Tugan (2021) <doi:10.1093/ectj/utaa021> for panel vector autoregression (VAR) models with interactive fixed effects. Provides joint estimation of VAR coefficients, latent common factors, and factor loadings via an iterative algorithm that alternates between principal component estimation of the factors and least squares estimation of the VAR coefficients, following the approach of Bai (2009) <doi:10.3982/ECTA6135>. Supports impulse response functions under recursive (Cholesky) identification, parametric confidence bands from the joint asymptotic distribution of the estimator (Theorem 2.3), and a classical residual bootstrap for robustness checks.

This package provides deep learning models for right-censored survival data using the torch backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>. The package is described in El Badisy (2026) <doi:10.32614/RJ-2026-008>.

This package provides functions for stabilometric signal quantification. The input is a data frame containing the x, y coordinates of the center-of-pressure displacement. Jose Magalhaes de Oliveira (2017) <doi:10.3758/s13428-016-0706-4> "Statokinesigram normalization method"; T E Prieto, J B Myklebust, R G Hoffmann, E G Lovett, B M Myklebust (1996) <doi:10.1109/10.532130> "Measures of postural steadiness: Differences between healthy young and elderly adults"; L F Oliveira et al (1996) <doi:10.1088/0967-3334/17/4/008> "Calculation of area of stabilometric signals using principal component analisys".

This package provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class trackeRdata (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) <doi: 10.18637/jss.v082.i07>, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.

Estimates the Vevea and Hedges (1995) weight-function model. By specifying arguments, users can also estimate the modified model described in Vevea and Woods (2005), which may be more practical with small datasets. Users can also specify moderators to estimate a linear model. The package functionality allows users to easily extract the results of these analyses as R objects for other uses. In addition, the package includes a function to launch both models as a Shiny application. Although the Shiny application is also available online, this function allows users to launch it locally if they choose.

Structural equation modeling (SEM) has a long history of representing models graphically as path diagrams. The semPlot package for R fills the gap between advanced, but time-consuming, graphical software and the limited graphics produced automatically by SEM software. In addition, semPlot offers more functionality than drawing path diagrams: it can act as a common ground for importing SEM results into R. Any result usable as input to semPlot can also be represented in any of the three popular SEM frame-works, as well as translated to input syntax for the R packages sem and lavaan.

This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG.

StabMap performs single cell mosaic data integration by first building a mosaic data topology, and for each reference dataset, traverses the topology to project and predict data onto a common embedding. Mosaic data should be provided in a list format, with all relevant features included in the data matrices within each list object. The output of stabMap is a joint low-dimensional embedding taking into account all available relevant features. Expression imputation can also be performed using the StabMap embedding and any of the original data matrices for given reference and query cell lists.

The `TrIdent` R package automates the analysis of transductomics data by detecting, classifying, and characterizing read coverage patterns associated with potential transduction events. Transductomics is a DNA sequencing-based method for the detection and characterization of transduction events in pure cultures and complex communities. Transductomics relies on mapping sequencing reads from a viral-like particle (VLP)-fraction of a sample to contigs assembled from the metagenome (whole-community) of the same sample. Reads from bacterial DNA carried by VLPs will map back to the bacterial contigs of origin creating read coverage patterns indicative of ongoing transduction.

Providing the functions for communicating with Amazon Web Services(AWS) Elastic Compute Cloud(EC2) and Elastic Container Service(ECS). The functions will have the prefix ecs_ or ec2_ depending on the class of the API. The request will be sent via the REST API and the parameters are given by the function argument. The credentials can be set via aws_set_credentials'. The EC2 documentation can be found at <https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Welcome.html> and ECS can be found at <https://docs.aws.amazon.com/AmazonECS/latest/APIReference/Welcome.html>.

This toolkit implements a numerical solution algorithm to invert a quality of life measure from observed data. Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictionsâ generated by idiosyncratic tastes and local ties â and trade frictions â generated by trade costs and non-tradable services, thereby reducing non-classical measurement error. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024) <https://econpapers.repec.org/RePEc:boc:bocode:s459382> "Measuring Quality of Life under Spatial Frictions". When using this programme or the toolkit in your work, please cite the paper.

Concept drift refers to the change in the data distribution or in the relationships between variables over time. drifter calculates distances between variable distributions or variable relations and identifies both types of drift. Key functions are: calculate_covariate_drift() checks distance between corresponding variables in two datasets, calculate_residuals_drift() checks distance between residual distributions for two models, calculate_model_drift() checks distance between partial dependency profiles for two models, check_drift() executes all checks against drift. drifter is a part of the DrWhy.AI universe (Biecek 2018) <arXiv:1806.08915>.

Descriptive analysis is essential for publishing medical articles. This package provides an easy way to conduct the descriptive analysis. 1. Both numeric and factor variables can be handled. For numeric variables, normality test will be applied to choose the parametric and nonparametric test. 2. Both two or more groups can be handled. For groups more than two, the post hoc test will be applied, Tukey for the numeric variables and FDR for the factor variables. 3. T test, ANOVA or Fisher test can be forced to apply. 4. Mean and standard deviation can be forced to display.

Developed by CDC/ATSDR (Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry), Social Vulnerability Index (SVI) serves as a tool to assess the resilience of communities by taking into account socioeconomic and demographic factors. Provided with year(s), region(s) and a geographic level of interest, findSVI retrieves required variables from US census data and calculates SVI for communities in the specified area based on CDC/ATSDR SVI documentation. Reference for the calculation methods: Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) <doi:10.2202/1547-7355.1792>.

Guild AI is an open-source tool for managing machine learning experiments. It's for scientists, engineers, and researchers who want to run scripts, compare results, measure progress, and automate machine learning workflow. Guild AI is a light weight, external tool that runs locally. It works with any framework, doesn't require any changes to your code, or access to any web services. Users can easily record experiment metadata, track model changes, manage experiment artifacts, tune hyperparameters, and share results. Guild AI combines features from Git', SQLite', and Make to provide a lab notebook for machine learning.

Fits area-level Hierarchical Bayesian Small Area Estimation models. The methodological foundation follows the standard area-level Small Area Estimation literature, primarily Rao and Molina (2015, ISBN: 9781118735787) <doi:10.1002/9781118735855>, while computational implementation is adapted to the parameterisation and prior-specification conventions of the brms package <doi:10.18637/jss.v080.i01>, which targets the Stan back-end. Supports a principled Bayesian workflow <doi:10.48550/arXiv.2011.01808>, with prior predictive checks, convergence diagnostics, model comparison, spatial random effects, custom distributions, missing-data handling, and a bilingual shiny application for non-programmer analysts.

Implementation of the HespDiv framework for hierarchical spatial subdivision of geographical occurrence data. The main function hespdiv() performs iterative spatially constrained subdivision of a study area to identify topologically contiguous clusters in geographic space using user-defined or preset subdivision methods. Additional functions provide tools for analysing subdivision results, visualizing hierarchical spatial structures, and evaluating robustness through sensitivity analyses and statistical testing. Some examples use the optional HDData data package, which is available from GitHub at Liudas-Dau/hespdiv_data. The methodology is described in Daumantas and Spiridonov (2024) <doi:10.1111/pala.12702>.

Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) <doi:10.1002/bimj.201900310> and Harrar and Cui (2022) <doi:10.1016/j.jspi.2022.05.009>. Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals.

Snow water equivalent is modeled with the process based models delta.snow and HS2SWE and empirical regression, which use relationships between density and diverse at-site parameters. The methods are described in Winkler et al. (2021) <doi:10.5194/hess-25-1165-2021>, Magnusson et al. (2025) <doi:10.1016/j.coldregions.2025.104435>, Guyennon et al. (2019) <doi:10.1016/j.coldregions.2019.102859>, Pistocchi (2016) <doi:10.1016/j.ejrh.2016.03.004>, Jonas et al. (2009) <doi:10.1016/j.jhydrol.2009.09.021> and Sturm et al. (2010) <doi:10.1175/2010JHM1202.1>.

An add-on to the party package, with a faster implementation of the partial-conditional permutation importance for random forests. The standard permutation importance is implemented exactly the same as in the party package. The conditional permutation importance can be computed faster, with an option to be backward compatible to the party implementation. The package is compatible with random forests fit using the party and the randomForest package. The methods are described in Strobl et al. (2007) <doi:10.1186/1471-2105-8-25> and Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.

Bayesian clustering of spatial regions with similar functional shapes using spanning trees and latent Gaussian models. The method enforces spatial contiguity within clusters and supports a wide range of latent Gaussian models, including non-Gaussian likelihoods, via the R-INLA framework. The algorithm is based on Zhong, R., Chacón-Montalván, E. A., and Moraga, P. (2026) <doi:10.1002/sim.70597>, extending the approach of Zhang, B., Sang, H., Luo, Z. T., and Huang, H. (2023) <doi:10.1214/22-AOAS1643>. The package includes tools for model fitting, convergence diagnostics, visualization, and summarization of clustering results.

Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating the squared deviations from sample variance. Besides indicating the contribution of squared deviations for the sample variability, these plots are capable of detecting characteristics of the distribution such as symmetry, skewness and outliers. A remarkable graphical method based on Sv-plot2 can determine the decision on testing hypotheses over one or two population means. In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.

This package provides a system for querying, retrieving and analyzing protocol- and results-related information on clinical trials from three public registers, the European Union Clinical Trials Register (EUCTR), ClinicalTrials.gov (CTGOV) and the ISRCTN. Trial information is downloaded, converted and stored in a database. Functions are included to identify deduplicated records, to easily find and extract variables (fields) of interest even from complex nesting as used by the registers, and to update previous queries. The package can be used for meta-analysis and trend-analysis of the design and conduct as well as for results of clinical trials.