This package provides a collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform.

This package provides the setup and calculations needed to run a likelihood-based continual reassessment method (CRM) dose finding trial and performs simulations to assess design performance under various scenarios. 3 dose finding designs are included in this package: ordinal proportional odds model (POM) CRM, ordinal continuation ratio (CR) model CRM, and the binary 2-parameter logistic model CRM. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous dose levels, combining ordinal grades 0 and 1 into one category, and incorporate safety and/or stopping rules. For POM and CR model designs, ordinal toxicity grades are specified by common terminology criteria for adverse events (CTCAE) version 4.0. Function pseudodata creates the necessary starting models for these 3 designs, and function nextdose estimates the next dose to test in a cohort of patients for a target DLT rate. We also provide the function crmsimulations to assess the performance of these 3 dose finding designs under various scenarios.

This package provides a tool for visualizing numerical data (e.g., gene expression, protein abundance) on predefined anatomical maps of human/mouse organs and subcellular organelles. It supports customization of color schemes, filtering by organ systems (for organisms) or organelle types, and generation of optional bar charts for quantitative comparison. The package integrates coordinate data for organs and organelles to plot anatomical/subcellular contours, mapping data values to specific structures for intuitive visualization of biological data distribution.The underlying method was described in the preprint by Zhou et al. (2022) <doi:10.1101/2022.09.07.506938>.

Interface to OpenStreetMap API for fetching and saving data from/to the OpenStreetMap database (<https://wiki.openstreetmap.org/wiki/API_v0.6>).

Inspired by S-PLUS function objects.summary(), provides a function with the same name that returns data class, storage mode, mode, type, dimension, and size information for R objects in the specified environment. Various filtering and sorting options are also proposed.

Access and Analyze Official Development Assistance (ODA) data using the OECD API <https://gitlab.algobank.oecd.org/public-documentation/dotstat-migration/-/raw/main/OECD_Data_API_documentation.pdf>. ODA data includes sovereign-level aid data such as key aggregates (DAC1), geographical distributions (DAC2A), project-level data (CRS), and multilateral contributions (Multisystem).

This package provides methods to generate a design in the input space that sequentially fills the output space of a black-box function. The output space-filling designs are helpful in inverse design or feature-based modeling problems. See Wang, Shangkun, Adam P. Generale, Surya R. Kalidindi, and V. Roshan Joseph. (2024), Sequential designs for filling output spaces, Technometrics, 66, 65â 76. for details. This work is supported by U.S. National Foundation grant CMMI-1921646.

Shiny Application to visualize Olympic Data. From 1896 to 2016. Even Winter Olympics events are included. Data is from Kaggle at <https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results>.

An interface to easily run local language models with Ollama <https://ollama.com> server and API endpoints (see <https://github.com/ollama/ollama/blob/main/docs/api.md> for details). It lets you run open-source large language models locally on your machine.

This package implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models, see Deldossi and Nai Ruscone (2020) <doi:10.18637/jss.v094.i02>.

Calculating the stability of random forest with certain numbers of trees. The non-linear relationship between stability and numbers of trees is described using a logistic regression model and used to estimate the optimal number of trees.

Extend the tidymodels ecosystem <https://www.tidymodels.org/> to enable the creation of predictive models with offset terms. Models with offsets are most useful when working with count data or when fitting an adjustment model on top of an existing model with a prior expectation. The former situation is common in insurance where data is often weighted by exposures. The latter is common in life insurance where industry mortality tables are often used as a starting point for setting assumptions.

This package provides functions for extracting text and tables from PDF-based order documents. It provides an n-gram-based approach for identifying the language of an order document. It furthermore uses R-package pdftools to extract the text from an order document. In the case that the PDF document is only including an image (because it is scanned document), R package tesseract is used for OCR. Furthermore, the package provides functionality for identifying and extracting order position tables in order documents based on a clustering approach.

This package provides a set of binary operators for common tasks such as regex manipulation.

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.

Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends.

All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution.

Obtain optimum block from Non-overlapping Block Bootstrap method.

This package provides tools for surrogate evaluation in longitudinal studies using state-space models as proposed in Santos Jr. and Parast (2026)<doi:10.48550/arXiv.2604.12882>. The package estimates treatment effects over time with and without adjustment for surrogate information, summarizes the proportion of treatment effect explained by a longitudinal surrogate, quantifies uncertainty via bootstrap resampling, and provides plotting and summary utilities for fitted models.

Model mixed integer linear programs in an algebraic way directly in R. The model is solver-independent and thus offers the possibility to solve a model with different solvers. It currently only supports linear constraints and objective functions. See the ompr website <https://dirkschumacher.github.io/ompr/> for more information, documentation and examples.

Tests the observed overlapping polygon area in a collection of polygons against a null model of random rotation, as explained in De la Cruz et al. (2017) <doi:10.13140/RG.2.2.12825.72801>.

Exposes some of the available OpenCV <https://opencv.org/> algorithms, such as a QR code scanner, and edge, body or face detection. These can either be applied to analyze static images, or to filter live video footage from a camera device.

Install and control Open Source Routing Machine ('OSRM') backend executables to prepare routing data and run/stop a local OSRM server. For computations with the running server use the osrm R package (<https://cran.r-project.org/package=osrm>).

This package implements a moment-targeting normality transformation based on the simultaneous optimization of Tukey g-h distribution parameters. The method is designed to minimize both asymmetry (skewness) and excess peakedness (kurtosis) in non-normal data by mapping it to a standard normal distribution Cebeci et al (2026) <doi:10.3390/sym18030458>. Optimization is performed by minimizing an objective function derived from the Anderson-Darling goodness-of-fit statistic with Stephens's correction factor, utilizing the L-BFGS-B algorithm for robust parameter estimation. This approach provides an effective alternative to power transformations like Box-Cox and Yeo-Johnson, particularly for data requiring precise tail-behavior adjustment.