Visualize as flow diagrams the logic of functions, expressions or scripts in a static way or when running a call, visualize the dependencies between functions or between modules in a shiny app, and more.

This package provides tools to support sensible statistics for functional response analysis.

This package provides functions for finding smooth interpolating curves connecting a series of points in the plane. Curves may be open or closed, that is, with the first and last point of the curve at the initial point.

This package provides functions to estimate a factor model using discrete and continuous proxy variables. The function dproxyme estimates a factor model of discrete proxy variables using an EM algorithm (Dempster, Laird, Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>; Hu (2008) <doi:10.1016/j.jeconom.2007.12.001>; Hu(2017) <doi:10.1016/j.jeconom.2017.06.002> ). The function cproxyme estimates a linear factor model (Cunha, Heckman, and Schennach (2010) <doi:10.3982/ECTA6551>).

R shiny app to perform data analysis and visualization for the Fully Automated Senescence Test (FAST) workflow.

This package creates structured R and Quarto projects with a consistent directory layout: scripts in R/, analysis documents in analysis/, and web assets in www/. The primary entry point, init(), downloads the latest template from a companion GitHub repository so that project structure evolves independently of package releases. Supports persistent author metadata and Quarto brand configuration that carry across projects automatically.

Serves as a companion toolkit for the formr survey framework (<https://rforms.org>). The package acts as a bridge between a formr server and a local R environment. Key features include an API client for fetching, type-casting, and automatically scoring data; a project management workflow for syncing study assets (surveys, CSS) for local editing; and functions for use within formr runs to generate dynamic, personalized feedback plots and to simplify survey logic.

This package provides a collection of functions for testing various aspects of univariate time series including independence and neglected nonlinearities. Further provides functions to investigate the chaotic behavior of time series processes and to simulate different types of chaotic time series maps.

The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. 15) functions to compute power and sample size for four Gamma-frailty-based designs: Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and General Joint Frailty Models. Each design includes two primary functions: a power function, which computes power given a specified sample size; and a sample size function, which computes the required sample size to achieve a specified power. 16) Weibull Illness-Death model with or without shared frailty between transitions. Left-truncated and right-censored data are allowed. 17) Weibull Competing risks model with or without shared frailty between the transitions. Left-truncated and right-censored data are allowed. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

An R interface for generating features for a cohort using data in the Common Data Model. Features can be constructed using default or custom made feature definitions. Furthermore it's possible to aggregate features and get the summary statistics.

This package implements methods for analyzing competing risks data in multi-center survival studies using frailty models. The approach relies on a mixed proportional hazards model for the sub-distribution, allowing for cluster-specific random effects. The package provides tools for model estimation with or without frailty using Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML). It supports flexible modeling of between-center heterogeneity and is particularly suited for multi-center clinical trials or registries. Core features include data simulation, likelihood computation, cluster-dependent censoring options, and testing of frailty effects. For methodological details, see Katsahian et al. (2006) <doi:10.1002/sim.2684>.

Processing of large-in-memory/large-on disk rasters and spatial vectors using GRASS <https://grass.osgeo.org/>. Most functions in the terra package are recreated. Processing of medium-sized and smaller spatial objects will nearly always be faster using terra or sf', but for large-in-memory/large-on-disk objects, fasterRaster may be faster. To use most of the functions, you must have the stand-alone version (not the OSGeoW4 installer version) of GRASS 8.0 or higher.

This package provides R bindings to the funz-fz Python package using reticulate'. The fz framework wraps arbitrary simulation codes to run parameter sweeps, design-of-experiments studies, and iterative algorithm-driven analyses by substituting variable placeholders in text input files and collecting outputs into data frames. Calculators can run locally (shell), over SSH, or on SLURM clusters. See <https://github.com/Funz/fz> for the underlying framework.

The user can directly compute and display false discovery rates from inputted p-values or z-scores under a variety of assumptions. p.fdr() computes FDRs, adjusted p-values and decision reject vectors from inputted p-values or z-values. get.pi0() estimates the proportion of data that are truly null. plot.p.fdr() plots the FDRs, adjusted p-values, and the raw p-values points against their rejection threshold lines.

This package provides allele frequency data for Short Tandem Repeat human genetic markers commonly used in forensic genetics for human identification and kinship analysis. Includes published population frequency data from the US National Institute of Standards and Technology, Federal Bureau of Investigation and the UK government.

Accompanies a paper (Barunik, Krehlik (2018) <doi:10.1093/jjfinec/nby001>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.

Collect your data on digital marketing campaigns from Google Analytics using the Windsor.ai API <https://windsor.ai/api-fields/>.

Miscellaneous utilities, tools and helper functions for finding and searching files on disk, searching for and removing R objects from the workspace. Does not import or depend on any third party package, but on core R only (i.e. it may depend on packages with priority base').

Rcpp (free of Java'/'Weka') implementation of FSelector entropy-based feature selection algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) <https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf> with a sparse matrix support.

Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates. Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.

Efficient algorithms for performing, updating, and removing rows or columns from the QR decomposition, R decomposition, or the inverse of the R decomposition of a matrix as rows or columns are added or removed. It also includes functions for solving linear systems of equations, normal equations for linear regression models, and normal equations for linear regression with a RIDGE penalty. For a detailed introduction to these methods, the monograph Matrix Computations (2013, <doi:10.1007/978-3-319-05089-8>) for complete introduction to the methods.

Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and fmeffects computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022) <doi:10.48550/arXiv.2201.08837>.

Finds CRAN packages by the topic requested. The topic can be given as a character string or as a regular expression and will help users to locate CRAN packages matching their specified requirement. findPackage(<string>) returns a data frame of packages with description containing the input string.

This package provides a game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. As board game published by Milton Bradley, designed by Howard Wexler and Ned Strongin.