R implementation of the FAIR Data Pipeline API'. The FAIR Data Pipeline is intended to enable tracking of provenance of FAIR (findable, accessible and interoperable) data used in epidemiological modelling.

Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described in Tonidandel & LeBreton (2015) <DOI:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <DOI:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes the total variance predicted in a regression model into weights that accurately reflect the proportional contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios, RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.

This package provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).

This package provides a set of functions to facilitate building formatted strings under various replacement rules: C-style formatting, variable-based formatting, and number-based formatting. C-style formatting is basically identical to built-in function sprintf'. Variable-based formatting allows users to put variable names in a formatted string which will be replaced by variable values. Number-based formatting allows users to use index numbers to represent the corresponding argument value to appear in the string.

Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.

Stan implementation of the Theory of Visual Attention (TVA; Bundesen, 1990; <doi:10.1037/0033-295X.97.4.523>) and numerous convenience functions for generating, compiling, fitting, and analyzing TVA models.

Computes the ridge partial correlation coefficients in a high or ultra-high dimensional linear regression problem. An extended Bayesian information criterion is also implemented for variable selection. Users provide the matrix of covariates as a usual dense matrix or a sparse matrix stored in a compressed sparse column format. Detail of the method is given in the manual.

Used for generating randomized community matrices under strict range cohesion. The package can handle data where species occurrence are recorded across sites ordered along gradients such as elevation and latitude, as well as species occurrences recorded on spatial grids with known geographic coordinates.

R access to hundreds of millions data series from DBnomics API (<https://db.nomics.world/>).

Captures errors encountered when running run_examples()', and processes and archives them. The function run_examples() within the devtools package allows batch execution of all of the examples within a given package. This is much more convenient than testing each example manually. However, a major inconvenience is that if an error is encountered, the program stops and does not complete testing the remaining examples. Also, there is not a systematic record of the results, namely which package functions had no examples, which had examples that failed, and which had examples that succeeded. The current package provides the missing functionality.

Estimates robust rank-based fixed effects and predicts robust random effects in two- and three- level random effects nested models. The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.

Native R interface to TMB (Template Model Builder) so models can be written entirely in R rather than C++'. Automatic differentiation, to any order, is available for a rich subset of R features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. RTMB provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

Set of functions for Regression Discontinuity Design ('RDD'), for data visualisation, estimation and testing.

This package provides a programmatic interface to the Web Service methods provided by the Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/developer/summary>). GBIF is a database of species occurrence records from sources all over the globe. rgbif includes functions for searching for taxonomic names, retrieving information on data providers, getting species occurrence records, getting counts of occurrence records, and using the GBIF tile map service to make rasters summarizing huge amounts of data.

This package provides popular sampling distributions C++ routines based in armadillo through a header file approach.

This package provides an infrastructure for handling multiple R Markdown reports, including automated curation and time-stamping of outputs, parameterisation and provision of helper functions to manage dependencies.

Rcpp bindings for PLANC', a highly parallel and extensible NMF/NTF (Non-negative Matrix/Tensor Factorization) library. Wraps algorithms described in Kannan et. al (2018) <doi:10.1109/TKDE.2017.2767592> and Eswar et. al (2021) <doi:10.1145/3432185>. Implements algorithms described in Welch et al. (2019) <doi:10.1016/j.cell.2019.05.006>, Gao et al. (2021) <doi:10.1038/s41587-021-00867-x>, and Kriebel & Welch (2022) <doi:10.1038/s41467-022-28431-4>.

This package provides a programmatic interface to the Species+ <https://speciesplus.net/> database via the Species+/CITES Checklist API <https://api.speciesplus.net/>.

Generation of Box-Cox based ROC curves and several aspects of inferences and hypothesis testing. Can be used when inferences for one biomarker (Bantis LE, Nakas CT, Reiser B. (2018)<doi:10.1002/bimj.201700107>) are of interest or when comparisons of two correlated biomarkers (Bantis LE, Nakas CT, Reiser B. (2021)<doi:10.1002/bimj.202000128>) are of interest. Provides inferences and comparisons around the AUC, the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff.

Programmatic interface to the Web Service methods provided by the National Phenology Network (<https://usanpn.org/>), which includes data on various life history events that occur at specific times.

Simple and fast tool for transforming phytosociological vegetation data into digital form for the following analysis. Danihelka, Chrtek, and Kaplan (2012, ISSN:00327786). Hennekens, and Schaminée (2001) <doi:10.2307/3237010>. Tichý (2002) <doi:10.1111/j.1654-1103.2002.tb02069.x>. Wickham, François, Henry, Müller (2022) <https://CRAN.R-project.org/package=dplyr>.

Interface for multiple data sources, such as the `EDDS` API <https://evds2.tcmb.gov.tr/index.php?/evds/userDocs> of the Central Bank of the Republic of Türkiye and the `FRED` API <https://fred.stlouisfed.org/docs/api/fred/> of the Federal Reserve Bank. Both data providers require API keys for access, which users can easily obtain by creating accounts on their respective websites. The package provides caching ability with the selection of periods to increase the speed and efficiency of requests. It combines datasets requested from different sources, helping users when the data has common frequencies. While combining data frames whenever possible, it also keeps all requested data available as separate data frames to increase efficiency.

This package produces Shiny applications for different types of popular functional data analyses. The functional data analyses are implemented in the refund package, then refund.shiny reads in the refund object and implements an object-specific set of plots based on the object class using S3.

Recursive partitioning methods to build classification trees for ordinal responses within the CART framework. Trees are grown using the Generalized Gini impurity function, where the misclassification costs are given by the absolute or squared differences in scores assigned to the categories of the response. Pruning is based on the total misclassification rate or on the total misclassification cost.