This package provides methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, <doi:10.1007/978-3-662-57380-8>). See citation("Rssa") for details.

This package contains basic tools for visualizing, interpreting, and building regression models. It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9.

This package provides robust statistical methods for analyzing numeric data, including robust estimation of location and scale using Huber M-estimators and a robust two-sample t-test. Methods are based on Huber (1981, ISBN:0471418056) "Robust Statistics" and Smyth (2004) <doi:10.2202/1544-6115.1027>.

This package provides tools to search, access, and format taxonomic information from The Reptile Database (<https://reptile-database.org>) directly within R'. Users can retrieve species-level data, distribution, etymology, synonyms, common names, and other relevant information for reptiles. Designed for taxonomists, ecologists, and biodiversity researchers.

Validates instantiated patient cohorts on an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database for real-world-evidence (RWE) studies. From a single function call it produces a structured validation report in Hypertext Markup Language (HTML) and JavaScript Object Notation (JSON) covering concept coverage, cohort attrition, temporal data density, and covariate feasibility against a comparator. The checks are aligned with the United States Food and Drug Administration (FDA) guidance on real-world data and evidence, FDA (2023) <https://www.fda.gov/media/171667/download>, the Harmonized Protocol Template to Enhance Reproducibility (HARPER), Wang and others (2022) <doi:10.1002/pds.5507>, and the Reporting of Studies Conducted Using Observational Routinely-Collected Data for Pharmacoepidemiology (RECORD-PE) statement, Langan and others (2018) <doi:10.1136/bmj.k3532>. A self-contained example database is bundled so the checks can be run without a live database connection.

Compute price indices using various Hedonic and multilateral methods, including Laspeyres, Paasche, Fisher, and HMTS (Hedonic Multilateral Time series re-estimation with splicing). The central function calculate_hedonic_index() offers a unified interface for running these methods on structured datasets. This package is designed to support index construction workflows across a wide range of domains â including but not limited to real estate â where quality-adjusted price comparisons over time are essential. The development of this package was funded by Eurostat and Statistics Netherlands (CBS), and carried out by Statistics Netherlands. The HMTS method implemented here is described in Ishaak, Ouwehand and Remøy (2024) <doi:10.1177/0282423X241246617>. For broader methodological context, see Eurostat (2013, ISBN:978-92-79-25984-5, <doi:10.2785/34007>).

R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.

Manually bin data using weight of evidence and information value. Includes other binning methods such as equal length, quantile and winsorized. Options for combining levels of categorical data are also available. Dummy variables can be generated based on the bins created using any of the available binning methods. References: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>.

Interaction with "RevBayes" via R. Objects created in "RevBayes" can be passed into the R environment, and many types can be converted into similar R objects. To download "RevBayes", go to <https://revbayes.github.io/download>.

Implementation of the Robust Exponential Decreasing Index (REDI), proposed in the article by Issa Moussa, Arthur Leroy et al. (2019) <https://bmjopensem.bmj.com/content/bmjosem/5/1/e000573.full.pdf>. The REDI represents a measure of cumulated workload, robust to missing data, providing control of the decreasing influence of workload over time. Various functions are provided to format data, compute REDI, and visualise results in a simple and convenient way.

This package provides a proof of concept implementation of regularized non-negative matrix factorization optimization. A non-negative matrix factorization factors non-negative matrix Y approximately as L R, for non-negative matrices L and R of reduced rank. This package supports such factorizations with weighted objective and regularization penalties. Allowable regularization penalties include L1 and L2 penalties on L and R, as well as non-orthogonality penalties. This package provides multiplicative update algorithms, which are a modification of the algorithm of Lee and Seung (2001) <http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf>, as well as an additive update derived from that multiplicative update. See also Pav (2024) <doi:10.48550/arXiv.2410.22698>.

This package performs goodness of fits tests for both high and low-dimensional linear models. It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity. In addition one can test the significance of potentially large groups of variables, and also produce p-values for the significance of individual variables in high-dimensional linear regression.

HTML formats and templates for rmarkdown documents, with some extra features such as automatic table of contents, lightboxed figures, dynamic crosstab helper.

Modern results of psychometric theory are implemented to provide users with a way of evaluating the internal structure of a set of items guided by theory. These methods are discussed in detail in VanderWeele and Padgett (2024) <doi:10.31234/osf.io/rnbk5>. The relative excess correlation matrices will, generally, have numerous negative entries even if all of the raw correlations between each pair of indicators are positive. The positive deviations of the relative excess correlation matrix entries help identify clusters of indicators that are more strongly related to one another, providing insights somewhat analogous to factor analysis, but without the need for rotations or decisions concerning the number of factors. A goal similar to exploratory/confirmatory factor analysis, but recmetrics uses novel methods that do not rely on assumptions of latent variables or latent variable structures.

This package provides a supportive collection of functions for gathering and plotting treatment ranking metrics after network meta-analysis.

This package provides functions to download and parse robots.txt files. Ultimately the package makes it easy to check if bots (spiders, crawler, scrapers, ...) are allowed to access specific resources on a domain.

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 a trimmed down copy of the "kent-core source tree" turned into a C library for manipulation of .2bit files. See <https://genome.ucsc.edu/FAQ/FAQformat.html#format7> for a quick overview of the 2bit format. The "kent-core source tree" can be found here: <https://github.com/ucscGenomeBrowser/kent-core/>. Only the .c and .h files from the source tree that are related to manipulation of .2bit files were kept. Note that the package is primarily useful to developers of other R packages who wish to use the 2bit C library in their own C'/'C++ code.

An interface to iDigBio's search API that allows downloading specimen records. Searches are returned as a data.frame. Other functions such as the metadata end points return lists of information. iDigBio is a US project focused on digitizing and serving museum specimen collections on the web. See <https://www.idigbio.org> for information on iDigBio.

This package provides a tidy, vectorized interface to the RxNorm / RxNav API for resolving drug names, RxCUIs, National Drug Codes (NDCs), and related drug concept metadata. The package supports workflows for mapping between drug names, RxCUIs, NDCs, ingredients, products, drug classes, and related concepts using data from the National Library of Medicine's RxNav services <https://lhncbc.nlm.nih.gov/RxNav/APIs/> and RxNorm <https://www.nlm.nih.gov/research/umls/rxnorm/>.

Efficiently parses, filters, and writes binary ITCH files (Version 5.0) containing detailed financial transactions as distributed by NASDAQ to a data.table. Includes functions to interact with NASDAQ data services at <https://emi.nasdaq.com/ITCH/> and <https://emi.nasdaq.com/ITCH/Stock_Locate_Codes/>.

Rank-hazard plots Karvanen and Harrell (2009) <DOI:10.1002/sim.3591> visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.

This package provides an interface from R to the autodiff library <https://autodiff.github.io/>, a modern header-only C++ library for automatic differentiation. Unlike numerical differentiation, automatic differentiation computes derivatives of functions to machine precision without truncation error, using either forward or reverse mode. The autodiff header files are shipped with this package so that other R packages can use them by including Rcppautodiff in the LinkingTo field of their DESCRIPTION file. Example programs demonstrate computing derivatives of single-variable and multi-variable functions, gradient vectors, Jacobian matrices and derivatives with respect to parameters, using Rcpp and RcppEigen'.

The RMM fits Revenue Management Models using the RDE(Robust Demand Estimation) method introduced in the paper by <doi:10.2139/ssrn.3598259>, one of the customer choice-based Revenue Management Model. Furthermore, it is possible to select a multinomial model as well as a conditional logit model as a model of RDE.