Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.

Various tests as roxygen2 roclets: e.g. testthat and tinytest tests. Also other static analysis tools as checking parameter documentation consistency and others.

Using a CSV, LaTeX and R to easily build attractive resumes.

This package provides tools to help with shiny reactivity. The react object offers an alternative way to call reactive expressions to better identify them in the server code.

Generate random user data from the Random User Generator API. For more information, see <https://randomuser.me/>.

R implementation of the common parsing tools lex and yacc'.

Recursive algorithms for computing various relatedness coefficients, including pairwise kinship, kappa and identity coefficients. Both autosomal and X-linked coefficients are computed. Founders are allowed to be inbred, which enables construction of any given kappa coefficients, as described in Vigeland (2020) <doi:10.1007/s00285-020-01505-x>. In addition to the standard coefficients, ribd also computes a range of lesser-known coefficients, including generalised kinship coefficients, multi-person coefficients and two-locus coefficients (Vigeland, 2023, <doi:10.1093/g3journal/jkac326>). Many features of ribd are available through the online app QuickPed at <https://magnusdv.shinyapps.io/quickped>; see Vigeland (2022) <doi:10.1186/s12859-022-04759-y>.

Import REDATAM formats into R via the Open REDATAM C++ library. The full context of this project and details about the implementation are available in <doi:10.1017/dap.2025.4> (Open Access).

Visualize networks using the javascript library roughjs'. This allows to draw sketchy, hand-drawn-like networks.

Set of analytical procedures based on advanced data analysis in support of Brazil's public sector external control activity.

This package provides a set of functions to see and interactively adjust a distribution of lessons by day, aiming at homogenizing individual distributions (for each class and teacher).

This package provides functions to perform robust stepwise split regularized regression. The approach first uses a robust stepwise algorithm to split the variables into the models of an ensemble. An adaptive robust regularized estimator is then applied to each subset of predictors in the models of an ensemble.

Displays palette of 5 colors based on photos depicting the unique and vibrant culture of Punjab in Northern India. Since Punjab translates to ``Land of 5 Rivers there are 5 colors per palette. If users need more than 5 colors, they can merge 2 to 3 palettes to create their own color-combination, or they can cherry-pick their own custom colors. Users can view up to 3 palettes together. Users can also list all the palette choices. And last but not least, users can see the photo that inspired a particular palette.

Insert/extract text "reminders" into/from function source code comments or as the "comment" attribute of any object. The former can be handy in development as reminders of e.g. argument requirements, expected objects in the calling environment, required options settings, etc. The latter can be used to provide information of the object and as simple manual "tooltips" for users, among other things.

This package provides a S4 class has been created such that complex operations can be executed on each cell of a raster map. The raster of objects contains a raster map with the addition of a list of generic objects: one object for each raster cells. It allows to write few lines of R code for complex map algebra. Two environmental applications about frequency analysis of raster map of precipitation and creation of a raster map of soil water retention curves have been presented.

We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.

External jars required for package RMOA. RMOA is a framework to build data stream models on top of MOA (Massive Online Analysis - <https://moa.cms.waikato.ac.nz/>). The jar files are put in this R package, the modelling logic can be found in the RMOA package.

Annotate text with entities and the relations between them. Annotate areas of interest in images with your labels. Providing htmlwidgets bindings to the recogito <https://github.com/recogito/recogito-js> and annotorious <https://github.com/recogito/annotorious> libraries.

Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.

Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval Method is designed to perform multi-criteria decision-making (MCDM), developed by Mališa Žižovic in 2020 (<doi:10.3390/math8061015>). It calculates the final sorted rankings based on a decision matrix where rows represent alternatives and columns represent criteria. The method uses: - A numeric vector of weights for each criterion (the sum of weights must be 1). - A numeric vector of ideal values for each criterion. - A numeric vector of anti-ideal values for each criterion. - Numeric values representing the extent to which the ideal value is preferred over the anti-ideal value, and the extent to which the anti-ideal value is considered worse. The function standardizes the decision matrix, normalizes the data, applies weights, and returns the final sorted rankings.

This package provides tools for working with Type S (Sign) and Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000) <doi:10.1007/s001800000040> and Gelman & Carlin (2014) <doi:10.1177/1745691614551642>. In addition to simply calculating the probability of Type S/M error, the package includes functions for calculating these errors across a variety of effect sizes for comparison, and recommended sample size given "tolerances" for Type S/M errors. To improve the speed of these calculations, closed forms solutions for the probability of a Type S/M error from Lu, Qiu, and Deng (2018) <doi:10.1111/bmsp.12132> are implemented. As of 1.0.0, this includes support only for simple research designs. See the package vignette for a fuller exposition on how Type S/M errors arise in research, and how to analyze them using the type of design analysis proposed in the above papers.

Client for various CrossRef APIs', including metadata search with their old and newer search APIs', get citations in various formats (including bibtex', citeproc-json', rdf-xml', etc.), convert DOIs to PMIDs', and vice versa', get citations for DOIs', and get links to full text of articles when available.

This package contains functions for simulating the linear fractional stable motion according to the algorithm developed by Mazur and Otryakhin <doi:10.32614/RJ-2020-008> based on the method from Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for estimation of parameters of these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different related quantities.

Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) <https://revistas.unimilitar.edu.co/index.php/rfcb/article/view/2037> Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico.