This package provides a series of utility functions to help with reshaping hierarchy of data tree, and reform the structure of data tree.

This package provides tools to support systematic and reproducible workflows for both stationary and nonstationary flood frequency analysis, with applications extending to other hydroclimate extremes, such as precipitation frequency analysis. This package implements the FFA framework proposed by Vidrio- Sahagún et al. (2024) <doi:10.1016/j.envsoft.2024.105940>, originally developed in MATLAB', now adapted for the R environment. This work was funded by the Flood Hazard Identification and Mapping Program of Environment and Climate Change Canada, as well as the Canada Research Chair (Tier 1) awarded to Dr. Pietroniro.

This package provides functions for importing, creating, editing and exporting FSK files <https://foodrisklabs.bfr.bund.de/fskx-food-safety-knowledge-exchange-format/> using the R programming environment. Furthermore, it enables users to run simulations contained in the FSK files and visualize the results.

Feature subset selection algorithms modularized in search algorithms and measure utilities.

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. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

Lognormal models have broad applications in various research areas such as economics, actuarial science, biology, environmental science and psychology. The estimation problem in lognormal models has been extensively studied. This R package fuel implements thirty-nine existing and newly proposed estimators. See Zhang, F., and Gou, J. (2020), A unified framework for estimation in lognormal models, Technical report.

The Futureverse is a set of packages for parallel and distributed process with the future package at its core, cf. Bengtsson (2021) <doi:10.32614/RJ-2021-048>. This package is designed to make it easy to install and load multiple Futureverse packages in a single step. This package is intended for end-users, interactive use, and R scripts. Packages must not list it as a dependency - instead, explicitly declare each Futureverse package as a dependency as needed.

Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for the further details). Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022) <doi:10.34768/amcs-2022-0021>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-031-08974-9_39>, Romaniuk et al. (2024) <doi:10.32614/RJ-2024-016>) together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap. The package also includes real-life datasets of epistemic fuzzy triangular and trapezoidal numbers. The fuzzy numbers used in this package are consistent with the FuzzyNumbers package.

Estimating the number of factors in Exploratory Factor Analysis (EFA) with out-of-sample prediction errors using a cross-validation scheme. Haslbeck & van Bork (Preprint) <https://psyarxiv.com/qktsd>.

Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the ForLion algorithm and EW ForLion algorithm. The algorithms will search for locally optimal designs and EW optimal designs under the D-criterion. Reference: Huang, Y., Li, K., Mandal, A., & Yang, J., (2024)<doi:10.1007/s11222-024-10465-x>.

This package provides functional tools such as fmap(), fwalk(), and fapply() to iterate over vectors, data frames, or grouped data with optional parallelism and real-time progress tracking. Designed for readable and reproducible workflows, including support for Monte Carlo simulations and benchmarking.

This package provides a function composition operator to chain a series of calls into a single function, mimicking the math notion of (f o g o h)(x) = h(g(f(x))). Inspired by pipeOp ('|>') since R4.1 and magrittr pipe ('%>%'), the operator build a pipe without putting data through, which is best for anonymous function accepted by utilities such as apply() and lapply().

Visualise sequential distributions using a range of plotting styles. Sequential distribution data can be input as either simulations or values corresponding to percentiles over time. Plots are added to existing graphic devices using the fan function. Users can choose from four different styles, including fan chart type plots, where a set of coloured polygon, with shadings corresponding to the percentile values are layered to represent different uncertainty levels. Full details in R Journal article; Abel (2015) <doi:10.32614/RJ-2015-002>.

This package provides the Big Merge Tracker and COSCI algorithms for convex clustering and feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details.

Generate regression results tables and plots in final format for publication. Explore models and export directly to PDF and Word using RMarkdown'.

This package provides a set of simplified functions for creating funnel plots for proportion data. This package supports user defined benchmarks, confidence limits and estimation methods (i.e. exact or approximate) based on Spiegelhalter (2005) <doi:10.1002/sim.1970>. Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored.

This package provides a convenient and user-friendly interface to interact with the Firebase Authentication REST API': <https://firebase.google.com/docs/reference/rest/auth>. It enables R developers to integrate Firebase Authentication services seamlessly into their projects, allowing for user authentication, account management, and other authentication-related tasks.

This package provides tools and features for "Exploratory Landscape Analysis (ELA)" of single-objective continuous optimization problems. Those features are able to quantify rather complex properties, such as the global structure, separability, etc., of the optimization problems.

Functional principal component analysis under the Linear Mixed Models representation of smoothing splines. The method utilizes the Demmler-Reinsch basis and assumes error independence. For more details see: F. Rosales (2016) <https://ediss.uni-goettingen.de/handle/11858/00-1735-0000-0028-87F9-6>.

Several functions to compute indicators for organization and efficiency in visual foraging, multi-target visual search, and cancellation tasks. The current version of this package includes the following indicators: best-r, mean Inter-target Distance, Percentage Above Optimal (PAO) scan path, and intersections in the scan path. For more detailed descriptions, see Mark et al. (2004) <doi:10.1212/01.WNL.0000131947.08670.D4>.

Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.

This package provides a study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the median survival time) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. Though The survival endpoint is often encountered in phase II trials, the existing SSD methods cannot deal with the survival endpoint. Furthermore, the existing SSD wonâ t control the type I error rate. The proposed designs can â partiallyâ control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal() function) for each arm. All the design needed components (sample size, operating characteristics) are supported.

An optimizer of Fused-Sparse Structural Equation Models, which is the state of the art jointly fused sparse maximum likelihood function for structural equation models proposed by Xin Zhou and Xiaodong Cai (2018 <doi:10.1101/466623>).

Computes implicitly induced quantities from prior/hyperparameter specifications of three Mixtures of Finite Mixtures models: Dirichlet Process Mixtures (DPMs; Escobar and West (1995) <doi:10.1080/01621459.1995.10476550>), Static Mixtures of Finite Mixtures (Static MFMs; Miller and Harrison (2018) <doi:10.1080/01621459.2016.1255636>), and Dynamic Mixtures of Finite Mixtures (Dynamic MFMs; Frühwirth-Schnatter, Malsiner-Walli and Grün (2020) <arXiv:2005.09918>). For methodological details, please refer to Greve, Grün, Malsiner-Walli and Frühwirth-Schnatter (2020) <arXiv:2012.12337>) as well as the package vignette.