An R wrapper for agena.ai <https://www.agena.ai> which provides users capabilities to work with agena.ai using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to agena.ai cloud or local API for calculations. Note: running calculations requires a valid agena.ai API license (past the initial trial period of the local API).

Calculates the optimal price of assets (such as airline flight seats, hotel room bookings) whose value becomes zero after a fixed ``expiry date''. Assumes potential customers arrive (possibly in groups) according to a known inhomogeneous Poisson process. Also assumes a known time-varying elasticity of demand (price sensitivity) function. Uses elementary techniques based on ordinary differential equations. Uses the package deSolve to effect the solution of these differential equations.

Presents a series of molecular and genetic routines in the R environment with the aim of assisting in analytical pipelines before and after the use of asreml or another library to perform analyses such as Genomic Selection or Genome-Wide Association Analyses. Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) <https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/ASRgenomics_Manual.pdf>.

Edit an Antares simulation before running it : create new areas, links, thermal clusters or binding constraints or edit existing ones. Update Antares general & optimization settings. Antares is an open source power system generator, more information available here : <https://antares-simulator.org/>.

Accessible wrappers for popular shiny UI components, enforcing ARIA attributes and structural requirements in line with BITV 2.0 (Barrierefreie-Informationstechnik-Verordnung) and WCAG 2.1 AA. Covers action buttons, text and select inputs, fluid page layouts with HTML landmarks and skip links, DT data tables, and bar and line graphs from ggplot2'. Components validate label presence, expose keyboard-accessible ARIA states, and provide a high-contrast toggle. This package was developed by d-fine GmbH on behalf of the German Federal Ministry of Research, Technology and Space (BMFTR).

Designed to help health economic modellers when building and reviewing models. The visualisation functions allow users to more easily review the network of functions in a project, and get lay summaries of them. The asserts included are intended to check for common errors, thereby freeing up time for modellers to focus on tests specific to the individual model in development or review. For more details see Smith and colleagues (2024)<doi:10.12688/wellcomeopenres.23180.1>.

Simulate clinical trials for diagnostic test devices and evaluate the operating characteristics under an adaptive design with futility assessment determined via the posterior predictive probabilities.

Fit, compare, and visualise linear and nonlinear regression models tailored to field-trial and dose-response agricultural data. Provides S3 classes for mixed-effects models (via lme4'), nonlinear growth curves (logistic, Gompertz', asymptotic, linear-plateau, quadratic), and four/five-parameter log-logistic dose-response models (via drc'). Includes automated starting-value heuristics, goodness-of-fit statistics, residual diagnostics, and ggplot2'-based visualisation. Methods are based on Bates and Watts (1988, ISBN:9780471816430), Ritz and others (2015) <doi:10.1371/journal.pone.0146021>, and Bates and others (2015) <doi:10.18637/jss.v067.i01>.

Calculating predictive model performance measures adjusted for predictor distributions using density ratio method (Sugiyama et al., (2012, ISBN:9781139035613)). L1 and L2 error for continuous outcome and C-statistics for binomial outcome are computed.

This toolkit implements a numerical solution algorithm to invert a quality of life measure from observed data. Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictionsâ generated by idiosyncratic tastes and local ties â and trade frictions â generated by trade costs and non-tradable services, thereby reducing non-classical measurement error. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024) <https://econpapers.repec.org/RePEc:boc:bocode:s459382> "Measuring Quality of Life under Spatial Frictions". When using this programme or the toolkit in your work, please cite the paper.

This package provides a framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.

This package provides a customisable set of tools for assessing and grading R or R-markdown scripts from students. It allows for checking correctness of code output, runtime statistics and static code analysis. The latter feature is made possible by representing R expressions using a tree structure.

Set of functions for analyzing Atomic Force Microscope (AFM) force-distance curves. It allows to obtain the contact and unbinding points, perform the baseline correction, estimate the Young's modulus, fit up to two exponential decay function to a stress-relaxation / creep experiment, obtain adhesion energies. These operations can be done either over a single F-d curve or over a set of F-d curves in batch mode.

This package provides functions to compute summary scores (besides proprietary ones) reported in the tabulated data resource that is released by the Adolescent Brain Cognitive Development (ABCD) study.

An application for analysis of Adverse Events, as described in Chen, et al., (2023) <doi:10.3390/cancers15092521>. The required data for the application includes demographics, follow up, adverse event, drug administration and optional tumor measurement data. The app can produce swimmers plots of adverse events, Kaplan-Meier plots and Cox Proportional Hazards model results for the association of adverse event biomarkers and overall survival and progression free survival. The adverse event biomarkers include occurrence of grade 3, low grade (1-2), and treatment related adverse events. Plots and tables of results are downloadable.

This package provides methods to construct frequentist confidence sets with valid marginal coverage for identifying the population-level argmin or argmax based on IID data. For instance, given an n by p loss matrixâ where n is the sample size and p is the number of modelsâ the CS.argmin() method produces a discrete confidence set that contains the model with the minimal (best) expected risk with desired probability. The argmin.HT() method helps check if a specific model should be included in such a confidence set. The main implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024) "Winners with confidence: Discrete argmin inference with an application to model selection".

This package provides a quick method for visualizing non-aggregated line-list or aggregated census data stratified by age and one or two categorical variables (e.g. gender and health status) with any number of values. It returns a ggplot object, allowing the user to further customize the output. This package is part of the R4Epis project <https://r4epis.netlify.app/>.

Leveraging Monte Carlo simulations, this package provides tools for diagnosing regression models. It implements a parametric bootstrap framework to compute statistics, generates diagnostic envelopes to assess goodness-of-fit, and evaluates type I error control for Wald tests. By simulating data under the assumption that the model is true, it helps to identify model mis-specifications and enhances the reliability of the model inferences.

This package provides methods to evaluate the performance characteristics of various point and interval estimators for optimal adaptive two-stage designs as described in Meis et al. (2024) <doi:10.1002/sim.10020>. Specifically, this package is written to work with trial designs created by the adoptr package (Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09>; Pilz et al. (2021) <doi:10.1002/sim.8953>)). Apart from the a priori evaluation of performance characteristics, this package also allows for the evaluation of the implemented estimators on real datasets, and it implements methods to calculate p-values.

Accompanies the book "Designing experiments and analyzing data: A model comparison perspective" (3rd ed.) by Maxwell, Delaney, & Kelley (2018; Routledge). Contains all of the data sets in the book's chapters and end-of-chapter exercises. Information about the book is available at <https://designingexperiments.com/>.

R interface for Apache Sedona based on sparklyr (<https://sedona.apache.org>).

Developer oriented utility functions designed to be used as the building blocks of R packages that work with ArcGIS Location Services. It provides functionality for authorization, Esri JSON construction and parsing, as well as other utilities pertaining to geometry and Esri type conversions. To support ArcGIS Pro users, authorization can be done via arcgisbinding'. Installation instructions for arcgisbinding can be found at <https://developers.arcgis.com/r-bridge/installation/>.

Gives some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson [see Fay and Kim <doi:10.1002/bimj.201600111>], sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test [with effect estimates and confidence intervals, see Fay and Malinovsky <doi:10.1002/sim.7890>], two-sample melding tests [see Fay, Proschan, and Brittain <doi:10.1111/biom.12231>], one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]), prevalence confidence intervals that adjust for sensitivity and specificity [see Lang and Reiczigel, 2014 <doi:10.1016/j.prevetmed.2013.09.015>] or Bayer, Fay, and Graubard, 2023 <doi:10.48550/arXiv.2205.13494>). The focus is on hypothesis tests that have compatible confidence intervals, but some functions only have confidence intervals (e.g., prevSeSp).

Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>, and Tong Lin, Jinseok Lee, Matt Helgeson, Megan T Valentine, Yimin Luo, Mengyang Gu (2026), <doi:10.48550/arXiv.2605.29424>.