This package provides functionality for carrying out sample size estimation and power calculation in Respondent-Driven Sampling.

Jade is a high performance template engine heavily influenced by Haml and implemented with JavaScript for node and browsers.

Create, Plot and Compare Replication Timing Profiles. The method is described in Muller et al., (2014) <doi: 10.1093/nar/gkt878>.

We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.

Image data used as examples in the loon R package.

This package provides a RUT (Rol Unico Tributario) is an unique and personal identification number implemented in Chile to identify citizens and taxpayers. Rutifier allows to validate if a RUT exist or not and change between the different formats a RUT can have.

ROSE (RObust Semiparametric Efficient) random forests for robust semiparametric efficient estimation in partially parametric models (containing generalised partially linear models). Details can be found in the paper by Young and Shah (2024) <doi:10.48550/arXiv.2410.03471>.

Pretty fast implementation of the Ramer-Douglas-Peucker algorithm for reducing the number of points on a 2D curve. Urs Ramer (1972), "An iterative procedure for the polygonal approximation of plane curves" <doi:10.1016/S0146-664X(72)80017-0>. David H. Douglas and Thomas K. Peucker (1973), "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature" <doi:10.3138/FM57-6770-U75U-7727>.

Streamlines data preprocessing, analysis, and visualization for association rule mining. Designed to work with the arules package, features include discretizing data frames, generating rule set intersections, and visualizing rules with heatmaps and Euler diagrams. RulesTools also includes a dataset on Brook trout detection from Nolan et al. (2022) <doi:10.1007/s13412-022-00800-x>.

Downloads spatial data from spatiotemporal asset catalogs ('STAC'), computes standard spectral indices from the Awesome Spectral Indices project (Montero et al. (2023) <doi:10.1038/s41597-023-02096-0>) against raster data, and glues the outputs together into predictor bricks. Methods focus on interoperability with the broader spatial ecosystem; function arguments and outputs use classes from sf and terra', and data downloading functions support complex CQL2 queries using rstac'.

Calculates the Iberian Actuarial Climate Index and its componentsâ including temperature, precipitation, wind power, and sea level dataâ to support climate change analysis and risk assessment. See "Zhou et al." (2023) <doi:10.26360/2023_3> for further details.

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.

An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065>. It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <doi:10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <doi:10.1016/j.csda.2007.11.006>).

This package provides utilities for the design and analysis of replication studies. Features both traditional methods based on statistical significance and more recent methods such as the sceptical p-value; Held L. (2020) <doi:10.1111/rssa.12493>, Held et al. (2022) <doi:10.1214/21-AOAS1502>, Micheloud et al. (2023) <doi:10.1111/stan.12312>. Also provides related methods including the harmonic mean chi-squared test; Held, L. (2020) <doi:10.1111/rssc.12410>, and intrinsic credibility; Held, L. (2019) <doi:10.1098/rsos.181534>. Contains datasets from five large-scale replication projects.

Transfer REDCap (Research Electronic Data Capture) data to a database, specifically optimized for DuckDB'. Processes data in chunks to handle large datasets without exceeding available memory. Features include data labeling, coded value conversion, and hearing a "quack" sound on success.

This package provides access to geocomputing and terrain analysis functions of the geographical information system (GIS) SAGA (System for Automated Geoscientific Analyses) from within R by running the command line version of SAGA. This package furthermore provides several R functions for handling ASCII grids, including a flexible framework for applying local functions (including predict methods of fitted models) and focal functions to multiple grids. SAGA GIS is available under GPL-2 / LGPL-2 licences from <https://sourceforge.net/projects/saga-gis/>.

This package provides a flexible alternative to the built-in rank() function called smartrank(). Optionally rank categorical variables by frequency (instead of in alphabetical order), and control whether ranking is based on descending/ascending order. smartrank() is suitable for both numerical and categorical data.

Analyses sentiment of a sentence in English and assigns score to it. It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative, Neutral. For a vector of sentences, it counts the number of sentences in each category of sentiment.In calculating the score, negation and various degrees of adjectives are taken into consideration. It deals only with English sentences.

An example package which shows use of NLopt functionality from C++ via Rcpp without requiring linking, and relying just on nloptr thanks to the exporting API added there by Jelmer Ypma. This package is a fully functioning, updated, and expanded version of the initial example by Julien Chiquet at <https://github.com/jchiquet/RcppArmadilloNLoptExample> also containing a large earlier pull request of mine.

The Rcpp package contains a C++ library that facilitates the integration of R and C++ in various ways via a rich API. This API was preceded by an earlier version which has been deprecated since 2010 (but is still supported to provide backwards compatibility in the package RcppClassic'). This package RcppClassicExamples provides usage examples for the older, deprecated API. There is also a corresponding package RcppExamples with examples for the newer, current API which we strongly recommend as the basis for all new development.

This package provides a user-friendly interface to NASA Exoplanets Archive API <https://exoplanetarchive.ipac.caltech.edu/>, enabling retrieval and analysis of exoplanetary and stellar data. Includes functions for querying, filtering, summarizing, and computing derived parameters from the Exoplanets catalog.

Generate utils::globalVariables() from roxygen2 @global and @autoglobal tags.

Applies methods used to estimate animal homerange, but instead of geospatial coordinates, we use isotopic coordinates. The estimation methods include: 1) 2-dimensional bivariate normal kernel utilization density estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon estimator, all applied to stable isotope data. Additionally, functions to determine niche area, polygon overlap between groups and levels (confidence contours) and plotting capabilities.

New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested and their performance accurately assessed. This requires densities that offer challenging properties to the novel sampling algorithms. One such popular problem is the Rosenbrock function. However, while its shape lends itself well to a benchmark problem, no codified multivariate expansion of the density exists. We have developed an extension to this class of distributions and supplied densities and direct sampler functions to assess the performance of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) <arXiv:1903.09556>.