This package provides a tool to create hydroclimate scenarios, stress test systems and visualize system performance in scenario-neutral climate change impact assessments. Scenario-neutral approaches stress-test the performance of a modelled system by applying a wide range of plausible hydroclimate conditions (see Brown & Wilby (2012) <doi:10.1029/2012EO410001> and Prudhomme et al. (2010) <doi:10.1016/j.jhydrol.2010.06.043>). These approaches allow the identification of hydroclimatic variables that affect the vulnerability of a system to hydroclimate variation and change. This tool enables the generation of perturbed time series using a range of approaches including simple scaling of observed time series (e.g. Culley et al. (2016) <doi:10.1002/2015WR018253>) and stochastic simulation of perturbed time series via an inverse approach (see Guo et al. (2018) <doi:10.1016/j.jhydrol.2016.03.025>). It incorporates Richardson-type weather generator model configurations documented in Richardson (1981) <doi:10.1029/WR017i001p00182>, Richardson and Wright (1984), as well as latent variable type model configurations documented in Bennett et al. (2018) <doi:10.1016/j.jhydrol.2016.12.043>, Rasmussen (2013) <doi:10.1002/wrcr.20164>, Bennett et al. (2019) <doi:10.5194/hess-23-4783-2019> to generate hydroclimate variables on a daily basis (e.g. precipitation, temperature, potential evapotranspiration) and allows a variety of different hydroclimate variable properties, herein called attributes, to be perturbed. Options are included for the easy integration of existing system models both internally in R and externally for seamless stress-testing'. A suite of visualization options for the results of a scenario-neutral analysis (e.g. plotting performance spaces and overlaying climate projection information) are also included. Version 1.0 of this package is described in Bennett et al. (2021) <doi:10.1016/j.envsoft.2021.104999>. As further developments in scenario-neutral approaches occur the tool will be updated to incorporate these advances.

Create secure, encrypted, and password-protected static HTML documents that include the machinery for secure in-browser authentication and decryption.

This package provides methods for performing fMRI quality assurance (QA) measurements of test objects. Heavily based on the fBIRN procedures detailed by Friedman and Glover (2006) <doi:10.1002/jmri.20583>.

Include assessing site classes based on the stand height growth and establishing a nonlinear mixed-effect biomass model under different site classes based on the whole stand model to achieve more accurate estimation of carbon sequestration. In particular, a carbon sequestration potential productivity calculation method based on the potential mean annual increment is proposed. This package is applicable to both natural forests and plantations. It can quantitatively assess standâ s potential productivity, realized productivity, and possible improvement under certain site, and can be used in many aspects such as site quality assessment, tree species suitability evaluation, and forest degradation evaluation. Reference: Lei X, Fu L, Li H, et al (2018) <doi:10.11707/j.1001-7488.20181213>. Fu L, Sharma R P, Zhu G, et al (2017) <doi:10.3390/f8040119>.

Finite element modeling of beam structures and 2D geometries using constant strain triangles. Applies material properties and boundary conditions (load and constraint) to generate a finite element model. The model produces stress, strain, and nodal displacements; a heat map is available to demonstrate regions where output variables are high or low. Also provides options for creating a triangular mesh of 2D geometries. Package developed with reference to: Bathe, K. J. (1996). Finite Element Procedures.[ISBN 978-0-9790049-5-7] -- Seshu, P. (2012). Textbook of Finite Element Analysis. [ISBN-978-81-203-2315-5] -- Mustapha, K. B. (2018). Finite Element Computations in Mechanics with R. [ISBN 9781315144474].

An implementation of a clustering algorithm for functional data based on adaptive density peak detection technique, in which the density is estimated by functional k-nearest neighbor density estimation based on a proposed semi-metric between functions. The proposed functional data clustering algorithm is computationally fast since it does not need iterative process. (Alex Rodriguez and Alessandro Laio (2014) <doi:10.1126/science.1242072>; Xiao-Feng Wang and Yifan Xu (2016) <doi:10.1177/0962280215609948>).

We facilitate the analysis of full factorial mating designs with mixed-effects models. The package contains six vignettes containing detailed examples.

This package provides methods for matrix factorization based on Wang and Stephens (2021) <https://jmlr.org/papers/v22/20-589.html>.

Fire behavior prediction models, including the Scott & Reinhardt's (2001) Rothermel Wildland Fire Modelling System <DOI:10.2737/RMRS-RP-29> and Alexander et al.'s (2006) Crown Fire Initiation & Spread model <DOI:10.1016/j.foreco.2006.08.174>. Also contains sample datasets, estimation of fire behavior prediction model inputs (e.g., fuel moisture, canopy characteristics, wind adjustment factor), results visualization, and methods to estimate fire weather hazard.

Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) <DOI:10.1007/s11222-019-09891-z> (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <DOI:10.1198/jcgs.2010.09008>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>).

This package provides a suite of functions to test for Functional Measurement Invariance (FMI) between two groups. Implements hierarchical permutation tests for configural, metric, and scalar invariance, adapting concepts from Multi-Group Confirmatory Factor Analysis (MGCFA) to functional data. Methods are based on concepts from: Meredith, W. (1993) <doi:10.1007/BF02294825>,5 Yao, F., Müller, H. G., & Wang, J. L. (2005) <doi:10.1198/016214504000001745>, and Lee, K. Y., & Li, L. (2022) <doi:10.1111/rssb.12471>.

Reads cell contents plus formatting from a spreadsheet file and creates an editable gt object with the same data and formatting. Supports the most commonly-used cell and text styles including colors, fills, font weights and decorations, and borders.

Plotting flood quantiles and their corresponding probabilities (return periods) on the probability papers. The details of relevant methods are available in Chow et al (1988, ISBN: 007070242X, 9780070702424), and Bobee and Ashkar (1991, ISBN: 0918334683, 9780918334688).

This package provides functions for performing (external) multidimensional unfolding. Restrictions (fixed coordinates or model restrictions) are available for both row and column coordinates in all combinations.

This package contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.

Enables high-dimensional penalized regression across heterogeneous subgroups. Fusion penalties are used to share information about the linear parameters across subgroups. The underlying model is described in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.

An R client for the Federal Reserve Economic Data ('FRED') API <https://research.stlouisfed.org/docs/api/>. Functions to retrieve economic time series and other data from FRED'.

The classical (i.e. Efron's, see Efron and Tibshirani (1994, ISBN:978-0412042317) "An Introduction to the Bootstrap") bootstrap is widely used for both the real (i.e. "crisp") and fuzzy data. The main aim of the algorithms implemented in this package is to overcome a problem with repetition of a few distinct values and to create fuzzy numbers, which are "similar" (but not the same) to values from the initial sample. To do this, different characteristics of triangular/trapezoidal numbers are kept (like the value, the ambiguity, etc., see Grzegorzewski et al. <doi:10.2991/eusflat-19.2019.68>, Grzegorzewski et al. (2020) <doi:10.2991/ijcis.d.201012.003>, Grzegorzewski et al. (2020) <doi:10.34768/amcs-2020-0022>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-030-95929-6_3>, Romaniuk and Hryniewicz (2019) <doi:10.1007/s00500-018-3251-5>). Some additional procedures related to these resampling methods are also provided, like calculation of the Bertoluzza et al.'s distance (aka the mid/spread distance, see Bertoluzza et al. (1995) "On a new class of distances between fuzzy numbers") and estimation of the p-value of the one- and two- sample bootstrapped test for the mean (see Lubiano et al. (2016, <doi:10.1016/j.ejor.2015.11.016>)). Additionally, there are procedures which randomly generate trapezoidal fuzzy numbers using some well-known statistical distributions.

This package provides a collection of utility functions for manipulating and analyzing factor vectors in R. It offers tools for filtering, splitting, combining, and reordering factor levels based on various criteria. The package is designed to simplify common tasks in categorical data analysis, making it easier to work with factors in a flexible and efficient manner.

This package contains functions for operations with fuzzy cognitive maps using t-norm and s-norm operators. T-norms and S-norms are described by Dov M. Gabbay and George Metcalfe (2007) <doi:10.1007/s00153-007-0047-1>. System indicators are described by Cox, Earl D. (1995) <isbn:1886801010>. Executable examples are provided in the "inst/examples" folder.

This package provides access to a range of functions for computing and visualizing the Full Bayesian Significance Test (FBST) and the e-value for testing a sharp hypothesis against its alternative, and the Full Bayesian Evidence Test (FBET) and the (generalized) Bayesian evidence value for testing a composite (or interval) hypothesis against its alternative. The methods are widely applicable as long as a posterior MCMC sample is available.

The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers, Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>, Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.

Use R as a minimal build system. This might come in handy if you are developing R packages and can not use a proper build system. Stay away if you can (use a proper build system).

This package implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.