An implementation of optimal weight exchange algorithm Yang(2013) <doi:10.1080/01621459.2013.806268> for three models. They are Crossover model with subject dropout, crossover model with proportional first order residual effects and interference model. You can use it to find either A-opt or D-opt approximate designs. Exact designs can be automatically rounded from approximate designs and relative efficiency is provided as well.

Generates n hierarchical clustering hypotheses on subsets of classifiers (usually species in community ecology studies). The n clustering hypotheses are combined to generate a generalized cluster, and computes three metrics of support. 1) The average proportion of elements conforming the group in each of the n clusters (integrity). And 2) the contamination, i.e., the average proportion of elements from other groups that enter a focal group. 3) The probability of existence of the group gives the integrity and contamination in a Bayesian approach.

Identifies an optimal transformation of a surrogate marker such that the proportion of treatment effect explained can be inferred based on the transformation of the surrogate and nonparametrically estimates two model-free quantities of this proportion. Details are described in Wang et al (2020) <doi:10.1093/biomet/asz065>.

This package provides tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.

An implementation of the Rapid Assessment Method for Older People or RAM-OP <https://www.helpage.org/resource/rapid-assessment-method-for-older-people-ramop-manual/>. It provides various functions that allow the user to design and plan the assessment and analyse the collected data. RAM-OP provides accurate and reliable estimates of the needs of older people.

Automated reporting in Word and PowerPoint can require customization for each organizational template. This package works around this by adding standard reporting functions and an abstraction layer to facilitate automated reporting workflows that can be replicated across different organizational templates.

This package provides functions for quickly creating R and Python scripts, as well as Rmarkdown or Quarto documents with automatically assigned name prefixes. Prefixes are either file counts (e.g. "001") or dates (e.g. "2022-09-26").

This package provides a comprehensive set of helpers that streamline data transmission and processing, making it effortless to interact with the OpenAI API.

This package provides tools for processing and analyzing data from the O-GlcNAcAtlas database <https://oglcnac.org/>, as described in Ma (2021) <doi:10.1093/glycob/cwab003>. It integrates UniProt <https://www.uniprot.org/> API calls to retrieve additional information. It is specifically designed for research workflows involving O-GlcNAcAtlas data, providing a flexible and user-friendly interface for customizing and downloading processed results. Interactive elements allow users to easily adjust parameters and handle various biological datasets.

Social media sites often embed cards when links are shared, based on metadata in the Open Graph Protocol (<https://ogp.me/>). This supports extracting that metadata from a website. It further allows for the creation of tags to add to a website to support the Open Graph Protocol and provides a list of the standard tags and their required properties.

Generating and validating One-time Password based on Hash-based Message Authentication Code (HOTP) and Time Based One-time Password (TOTP) according to RFC 4226 <https://datatracker.ietf.org/doc/html/rfc4226> and RFC 6238 <https://datatracker.ietf.org/doc/html/rfc6238>.

Download and import of OpenStreetMap ('OSM') data as sf or sp objects. OSM data are extracted from the Overpass web server (<https://overpass-api.de/>) and processed with very fast C++ routines for return to R'.

Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event outcome, and continuous outcome ODS design (Zhou et al. 2002) <doi: 10.1111/j.0006-341X.2002.00413.x>. Because ODS data has biased sampling nature, standard statistical analysis such as linear regression will lead to biases estimates of the population parameters. This package implements four statistical methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) <doi: 10.1111/j.1541-0420.2010.01500.x>. (3) Analyze a secondary outcome in continuous ODS design (Pan et al. 2018) <doi: 10.1002/sim.7672>. (4) An estimated likelihood method analyzing a secondary outcome in case-cohort data (Pan et al. 2017) <doi: 10.1111/biom.12838>.

Reads data from Bruker OPUS binary files of Fourier-Transform infrared spectrometers of the company Bruker Optics GmbH & Co. This package is released independently from Bruker, and Bruker and OPUS are registered trademarks of Bruker Optics GmbH & Co. KG. <https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/latest-release.html>. It lets you import both measurement data and parameters from OPUS files. The main method is `read_opus()`, which reads one or multiple OPUS files into a standardized list class. Behind the scenes, the reader parses the file header for assigning spectral blocks and reading binary data from the respective byte positions, using a reverse engineering approach. Infrared spectroscopy combined with chemometrics and machine learning is an established method to scale up chemical diagnostics in various industries and scientific fields.

An interface for interacting with OSF (<https://osf.io>). osfr enables you to access open research materials and data, or create and manage your own private or public projects.

An implementation for computing Optimal B-Robust Estimators of two-parameter distribution. The procedure is composed of some equations that are evaluated alternatively until the solution is reached. Some tools for analyzing the estimates are included. The most relevant is covariance matrix computation using a closed formula.

Efficient Monte Carlo Algorithms for the price and the sensitivities of Asian and European Options under Geometric Brownian Motion.

Detection of overdispersion in count data for multiple regression analysis. Log-linear count data regression is one of the most popular techniques for predictive modeling where there is a non-negative discrete quantitative dependent variable. In order to ensure the inferences from the use of count data models are appropriate, researchers may choose between the estimation of a Poisson model and a negative binomial model, and the correct decision for prediction from a count data estimation is directly linked to the existence of overdispersion of the dependent variable, conditional to the explanatory variables. Based on the studies of Cameron and Trivedi (1990) <doi:10.1016/0304-4076(90)90014-K> and Cameron and Trivedi (2013, ISBN:978-1107667273), the overdisp() command is a contribution to researchers, providing a fast and secure solution for the detection of overdispersion in count data. Another advantage is that the installation of other packages is unnecessary, since the command runs in the basic R language.

Microarray probe ID is not convenient for further enrichment analysis and target gene selection. The package is created for the rice microarray probe ID conversion. This package can convert microarray probe ID from GPL6864 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6864>, GPL8852 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL8852>, and GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" <https://rapdb.dna.affrc.go.jp> is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research. For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes. Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID.

This package implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include InterVA4 by Byass et al (2012) <doi:10.3402/gha.v5i0.19281>, InterVA5 by Byass at al (2019) <doi:10.1186/s12916-019-1333-6>, InSilicoVA by McCormick et al (2016) <doi:10.1080/01621459.2016.1152191>, NBC by Miasnikof et al (2015) <doi:10.1186/s12916-015-0521-2>, and a replication of Tariff method by James et al (2011) <doi:10.1186/1478-7954-9-31> and Serina, et al. (2015) <doi:10.1186/s12916-015-0527-9>. It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. The NBC method is implemented by the nbc4va package that can be installed from <https://github.com/rrwen/nbc4va>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the Tariff method.

Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) <doi:10.18637/jss.v083.i05>.

Designed to enhance data validation and management processes by employing a set of functions that read a set of rules from a CSV or Excel file and apply them to a dataset. Funded by the National Renewable Energy Laboratory and Possibility Lab, maintained by the Moore Institute for Plastic Pollution Research.

Visualise results obtained from analysing data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model using shiny applications.

This package provides tools for managing nested, multi-level configuration systems with runtime mutability, type validation, and default value management. Supports creating hierarchical options managers with customizable validators for scalar and vector types (numeric, character, logical), enumerated values, bounded ranges, and complex structures like XY pairs. Options can be dynamically modified at runtime while maintaining type safety through validator functions, and easily reset to their default values when needed.