Allows one to use Osmium Tool (<https://osmcode.org/osmium-tool/>) from R. Osmium is a multipurpose command line tool that enables one to manipulate and analyze OpenStreetMap files through several different commands. Currently, this package does not aim to offer functions that cover the entire Osmium API, instead making available functions that wrap only a very limited set of its features.

This tool enables the user to choose a randomization procedure based on sound scientific criteria. It comprises the generation of randomization sequences as well the assessment of randomization procedures based on carefully selected criteria. Furthermore, randomizeR provides a function for the comparison of randomization procedures.

This package provides functions to calculate Sample Number and Average Sample Number for Repetitive Group Sampling Plan Based on Cpk as given in Aslam et al. (2013) (<DOI:10.1080/00949655.2012.663374>).

Calculates relevance and significance values for simple models and for many types of regression models. These are introduced in Stahel, Werner A. (2021) "Measuring Significance and Relevance instead of p-values." <https://stat.ethz.ch/~stahel/relevance/stahel-relevance2103.pdf>. These notions are also applied to replication studies, as described in the manuscript Stahel, Werner A. (2022) "'Replicability': Terminology, Measuring Success, and Strategy" available in the documentation.

Extension to REddyProc that allows reading data from netCDF files.

R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.

The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. RGAP facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) <https://ideas.repec.org/p/euf/ecopap/0535.html>. To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. RGAP features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the AMECO <https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en> database and automated model selection procedures. See the paper by Streicher (2022) <http://hdl.handle.net/20.500.11850/552089> for details.

Fast alternatives to several relatively slow raster package functions. For large rasters, the functions run from 5 to approximately 100 times faster than the raster package functions they replace. The fasterize package, on which one function in this package depends, includes an implementation of the scan line algorithm attributed to Wylie et al. (1967) <doi:10.1145/1465611.1465619>.

Extracts tagged text from markdown manuscripts for inclusion in dynamically generated revision letters. Provides an R markdown template based on papaja::revision_letter_pdf() with comment cross-referencing, a system for managing multiple sections of extracted text, and a way to automatically determine the page number of quoted sections from PDF manuscripts.

Wrapper for the PoetryDB API <http://poetrydb.org> that allows for interaction and data extraction from the database in an R interface. The PoetryDB API is a database of poetry and poets implemented with MongoDB to enable developers and poets to easily access one of the most comprehensive poetry databases currently available.

Enhances the R Optimization Infrastructure ('ROI') package by registering the free GLPK solver. It allows for solving mixed integer linear programming ('MILP') problems as well as all variants/combinations of LP', IP'.

The goal of readsdr is to bridge the design capabilities from specialised System Dynamics software with the powerful numerical tools offered by R libraries. The package accomplishes this goal by parsing XMILE files ('Vensim and Stella') models into R objects to construct networks (graph theory); ODE functions for Stan'; and inputs to simulate via deSolve as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.

Accessible and flexible implementation of three ecoacoustic indices that are less commonly available in existing R frameworks: Background Noise, Soundscape Power and Soundscape Saturation. The functions were design to accommodate a variety of sampling designs. Users can tailor calculations by specifying spectrogram time bin size, amplitude thresholds and normality tests. By simplifying computation and standardizing reproducible methods, the package aims to support ecoacoustics studies. For more details about the indices read Towsey (2014) <doi:10.1016/j.procs.2014.05.063> and Burivalova (2017) <doi:10.1111/cobi.12968>.

Flexible rounding functions for use in error detection. They were outsourced from the scrutiny package.

This package provides a programmatic interface to the API provided by the iNaturalist website <https://www.inaturalist.org/> to download species occurrence data submitted by citizen scientists.

This package implements the methodology of "Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959--1035". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.

Robust covariance estimation for matrix-valued data and data with Kronecker-covariance structure using the Matrix Minimum Covariance Determinant (MMCD) estimators and outlier explanation using and Shapley values.

The SPRITE algorithm creates possible distributions of discrete responses based on reported sample parameters, such as mean, standard deviation and range (Heathers et al., 2018, <doi:10.7287/peerj.preprints.26968v1>). This package implements it, drawing heavily on the code for Nick Brown's rSPRITE Shiny app <https://shiny.ieis.tue.nl/sprite/>. In addition, it supports the modeling of distributions based on multi-item (Likert-type) scales and the use of restrictions on the frequency of particular responses.

Toolbox for remote sensing image processing and analysis such as calculating spectral indexes, principal component transformation, unsupervised and supervised classification or fractional cover analyses.

This package contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.

REDCap Data Management - REDCap (Research Electronic Data CAPture; <https://projectredcap.org>) is a web application developed at Vanderbilt University, designed for creating and managing online surveys and databases and the REDCap API is an interface that allows external applications to connect to REDCap remotely, and is used to programmatically retrieve or modify project data or settings within REDCap, such as importing or exporting data. REDCapDM is an R package that allows users to manage data exported directly from REDCap or using an API connection. This package includes several functions designed for pre-processing data, generating reports of queries such as outliers or missing values, and following up on previously identified queries.

Randomization-based inference for average treatment effects in potentially inexactly matched observational studies. It implements the inverse post-matching probability weighting framework proposed by the authors. The post-matching probability calculation follows the approach of Pimentel and Huang (2024) <doi:10.1093/jrsssb/qkae033>. The optimal full matching method is based on Hansen (2004) <doi:10.1198/106186006X137047>. The variance estimator extends the method proposed in Fogarty (2018) <doi:10.1111/rssb.12290> from the perfect randomization settings to the potentially inexact matching case. Comparisons are made with conventional methods, as described in Rosenbaum (2002) <doi:10.1007/978-1-4757-3692-2>, Fogarty (2018) <doi:10.1111/rssb.12290>, and Kang et al. (2016) <doi:10.1214/15-aoas894>.

This package provides a tool that allows to download and save historical time series data for future use offline. The intelligent updating functionality will only download the new available information; thus, saving you time and Internet bandwidth. It will only re-download the full data-set if any inconsistencies are detected. This package supports following data provides: Yahoo (finance.yahoo.com), FRED (fred.stlouisfed.org), Quandl (data.nasdaq.com), AlphaVantage (www.alphavantage.co), Tiingo (www.tiingo.com).

Facilitating the creation of reproducible statistical report templates. Once created, rapport templates can be exported to various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the converter backend.