This package provides a toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on switchBox package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret. For complex problems RF performs better. Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...). Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model. We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) <doi:10.1093/bioinformatics/btab088>.

Aho-Corasick is an optimal algorithm for finding many keywords in a text. It can locate all matches in a text in O(N+M) time; i.e., the time needed scales linearly with the number of keywords (N) and the size of the text (M). Compare this to the naive approach which takes O(N*M) time to loop through each pattern and scan for it in the text. This implementation builds the trie (the generic name of the data structure) and runs the search in a single function call. If you want to search multiple texts with the same trie, the function will take a list or vector of texts and return a list of matches to each text. By default, all 128 ASCII characters are allowed in both the keywords and the text. A more efficient trie is possible if the alphabet size can be reduced. For example, DNA sequences use at most 19 distinct characters and usually only 4; protein sequences use at most 26 distinct characters and usually only 20. UTF-8 (Unicode) matching is not currently supported.

Airport problems, introduced by Littlechild and Owen (1973) <https://www.jstor.org/stable/2629727>, are cost allocation problems where agents share the cost of a facility (or service) based on their ordered needs. Valid allocations must satisfy no-subsidy constraints, meaning that no group of agents contributes more than the highest cost of its members (i.e., no agent is allowed to subsidize another). A rule is a mechanism that selects an allocation vector for a given problem. This package computes several rules proposed in the literature, including both standard rules and their variants, such as weighted versions, rules for clones, and rules based on the agentsâ hierarchy order. These rules can be applied to various problems of interest, including the allocation of liabilities and the maintenance of irrigation systems, among others. Moreover, the package provides functions for graphical representation, enabling users to visually compare the outcomes produced by each rule, or to display the no-subsidy set. In addition, it includes four datasets illustrating different applications and examples of airport problems. For a more detailed explanation of all concepts, see Thomson (2024) <doi:10.1016/j.mathsocsci.2024.03.007>.

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>.

systemPipeShiny (SPS) extends the widely used systemPipeR (SPR) workflow environment with a versatile graphical user interface provided by a Shiny App. This allows non-R users, such as experimentalists, to run many systemPipeR’s workflow designs, control, and visualization functionalities interactively without requiring knowledge of R. Most importantly, SPS has been designed as a general purpose framework for interacting with other R packages in an intuitive manner. Like most Shiny Apps, SPS can be used on both local computers as well as centralized server-based deployments that can be accessed remotely as a public web service for using SPR’s functionalities with community and/or private data. The framework can integrate many core packages from the R/Bioconductor ecosystem. Examples of SPS’ current functionalities include: (a) interactive creation of experimental designs and metadata using an easy to use tabular editor or file uploader; (b) visualization of workflow topologies combined with auto-generation of R Markdown preview for interactively designed workflows; (d) access to a wide range of data processing routines; (e) and an extendable set of visualization functionalities. Complex visual results can be managed on a Canvas Workbench’ allowing users to organize and to compare plots in an efficient manner combined with a session snapshot feature to continue work at a later time. The present suite of pre-configured visualization examples. The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community.

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.

Support utilities for RSpec gems.

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

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

React is a JavaScript library for building user interfaces.

React is a JavaScript library for building user interfaces.

Platform Design Info for Affymetrix Clariom_S_Rat.

Shiny-based interactive gadgets of radial visualization methods and extensions thereof.

This package provides the Book-Crossing Dataset for the package recommenderlab.

This package provides Rake tasks to generate and check a manifest file.

Microarray gene expression data from the study of Vawter et al., 2004.

Affymetrix rta10 annotation data (chip rta10probeset) assembled using data from public repositories.

Platform Design Info for Affymetrix RaGene-2_0-st.

Platform Design Info for Affymetrix RaGene-2_1-st.

Rake-compiler provides a framework for building and packaging native C and Java extensions in Ruby.