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.

Response surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), "On the experimental attainment of optimum conditions" ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI: 10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts".). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). "Asymmetric rotatable designs and orthogonal transformations" ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) <DOI: 10.1080/02664763.2020.1864817>. "Sequential asymmetric third order rotatable designs (SATORDs)" .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model.

Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, <DOI:10.1016/j.artint.2020.103237>. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The comparison to common projection methods can be found in the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.

Four filters have been chosen namely haar', c6', la8', and bl14 (Kindly refer to wavelets in CRAN repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results are stored in input'. First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as MIN and other values are denoted as NA'. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as MAX and other values are denoted as NA'. output contains the similar number of rows (which is 8) and columns (which is number filter-level combinations) as of input'. Values in output are corresponding NA', MIN or MAX'. Finally, the column containing minimum number of NA values is denoted as the best ('FL'). In special case, if two columns having equal NA', it has been checked among these two columns which one is having least NA in first five rows and has been inferred as the best. FL_metrics_values are the corresponding metrics values. WARIGAANbest is the data frame (dimension: 1*8) containing different metrics of the best filter-level combination. More details can be found in Garai and others (2023) <doi:10.13140/RG.2.2.11977.42087>.

This package provides a plugin for the RuboCop code style enforcing & linting tool.

Xorshift random number generator.

Xorshift random number generator.

Xorshift random number generator

Support utilities for RSpec gems.

Web PKI X.509 Certificate Verification.

Types for rustdoc's json output.

Web PKI X.509 Certificate Verification.

Web PKI X.509 Certificate Verification.

Web PKI X.509 Certificate Verification.

Web PKI X.509 Certificate Verification.

Documentation at https://melpa.org/#/rails-routes

Documentation at https://melpa.org/#/realgud-rdb2

This package provides Typst's realization subsystem.

Derive implementation for ref_cast::RefCast.

This package provides portable, relative paths for Rust.

Event loop that drives Tokio I/O resources.

This package provides a Tokio-based asynchronous runtime.

Derive implementation for ref_cast::RefCast.

This package counts installed RPM packages using librpm.