This package provides a wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in caret package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) <doi:10.1287/ijoc.2022.0090>).

Package ACV (short for Affine Cross-Validation) offers an improved time-series cross-validation loss estimator which utilizes both in-sample and out-of-sample forecasting performance via a carefully constructed affine weighting scheme. Under the assumption of stationarity, the estimator is the best linear unbiased estimator of the out-of-sample loss. Besides that, the package also offers improved versions of Diebold-Mariano and Ibragimov-Muller tests of equal predictive ability which deliver more power relative to their conventional counterparts. For more information, see the accompanying article Stanek (2021) <doi:10.2139/ssrn.3996166>.

Construct time series for Germany's municipalities (Gemeinden) and districts (Kreise) using a annual crosswalk constructed by the Federal Office for Building and Regional Planning (BBSR).

The aligned rank transform for nonparametric factorial ANOVAs as described by Wobbrock, Findlater, Gergle, and Higgins (2011) <doi:10.1145/1978942.1978963>. Also supports aligned rank transform contrasts as described by Elkin, Kay, Higgins, and Wobbrock (2021) <doi:10.1145/3472749.3474784>.

An interface to Azure Queue Storage'. This is a cloud service for storing large numbers of messages, for example from automated sensors, that can be accessed remotely via authenticated calls using HTTP or HTTPS. Queue storage is often used to create a backlog of work to process asynchronously. Part of the AzureR family of packages.

This package provides tools supporting multi-criteria and group decision making, including variable number of criteria, by means of aggregation operators, spread measures, fuzzy logic connectives, fusion functions, and preordered sets. Possible applications include, but are not limited to, quality management, scientometrics, software engineering, etc.

This package provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. Including equal frequency binning, equidistant binning, K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, fully automatic generation of scorecards, etc. This package is designed to make scorecard development easier and faster. References include: 1. <http://shichen.name/posts/>. 2. Dong-feng Li(Peking University),Class PPT. 3. <https://zhuanlan.zhihu.com/p/389710022>. 4. <https://www.zhangshengrong.com/p/281oqR9JNw/>.

Utilities designed to make the analysis of field trials easier and more accessible for everyone working in plant breeding. It provides a simple and intuitive interface for conducting single and multi-environmental trial analysis, with minimal coding required. Whether you're a beginner or an experienced user, agriutilities will help you quickly and easily carry out complex analyses with confidence. With built-in functions for fitting Linear Mixed Models, agriutilities is the ideal choice for anyone who wants to save time and focus on interpreting their results. Some of the functions require the R package asreml for the ASReml software, this can be obtained upon purchase from VSN international <https://vsni.co.uk/software/asreml-r/>.

Imports Azure Application Insights for web pages into Shiny apps via Microsoft's JavaScript snippet. Allows app developers to submit page tracking and submit events.

Parse R code in a given directory for R packages and attempt to install them from CRAN or GitHub. Optionally use a dependencies file for tighter control over which package versions to install.

Addressing measurement error in covariates and misclassification in binary outcome variables within causal inference, the ATE.ERROR package implements inverse probability weighted estimation methods proposed by Shu and Yi (2017, <doi:10.1177/0962280217743777>; 2019, <doi:10.1002/sim.8073>). These methods correct errors to accurately estimate average treatment effects (ATE). The package includes two main functions: ATE.ERROR.Y() for handling misclassification in the outcome variable and ATE.ERROR.XY() for correcting both outcome misclassification and covariate measurement error. It employs logistic regression for treatment assignment and uses bootstrap sampling to calculate standard errors and confidence intervals, with simulated datasets provided for practical demonstration.

This package implements a multiple testing approach to the choice of a threshold gamma on the p-values using the Average Power Function (APF) and Bayes False Discovery Rate (FDR) robust estimation. Function apf_fdr() estimates both quantities from either raw data or p-values. Function apf_plot() produces smooth graphs and tables of the relevant results. Details of the methods can be found in Quatto P, Margaritella N, et al. (2019) <doi:10.1177/0962280219844288>.

This is an implementation of the Generalized Discrimination Score (also known as Two Alternatives Forced Choice Score, 2AFC) for various representations of forecasts and verifying observations. The Generalized Discrimination Score is a generic forecast verification framework which can be applied to any of the following verification contexts: dichotomous, polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble. A comprehensive description of the Generalized Discrimination Score, including all equations used in this package, is provided by Mason and Weigel (2009) <doi:10.1175/MWR-D-10-05069.1>.

Tracking accrual in clinical trials is important for trial success. If accrual is too slow, the trial will take too long and be too expensive. If accrual is much faster than expected, time sensitive tasks such as the writing of statistical analysis plans might need to be rushed. accrualPlot provides functions to aid the tracking of accrual and predict when a trial will reach it's intended sample size.

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.

Trigger animation effects on scroll on any HTML element of shiny and rmarkdown', such as any text or plot, thanks to the AOS Animate On Scroll jQuery library.

This package implements the Arellano-Bond estimation method combined with LASSO for dynamic linear panel models. See Chernozhukov et al. (2024) "Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models". arXiv preprint <doi:10.48550/arXiv.2402.00584>.

Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application of choice models in R. Users are able to write their own model functions or use a mix of already available ones. Random heterogeneity, both continuous and discrete and at the level of individuals and choices, can be incorporated for all models. There is support for both standalone models and hybrid model structures. Both classical and Bayesian estimation is available, and multiple discrete continuous models are covered in addition to discrete choice. Multi-threading processing is supported for estimation and a large number of pre and post-estimation routines, including for computing posterior (individual-level) distributions are available. For examples, a manual, and a support forum, visit <https://www.ApolloChoiceModelling.com>. For more information on choice models see Train, K. (2009) <isbn:978-0-521-74738-7> and Hess, S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview of the field.

Wraps the Abseil C++ library for use by R packages. Original files are from <https://github.com/abseil/abseil-cpp>. Patches are located at <https://github.com/doccstat/abseil-r/tree/main/local/patches>.

Adjusts output of cranlogs package to account for CRAN'-wide daily automated downloads and re-downloads caused by package updates.

Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the AutoNormalize library for Python by Alteryx (<https://github.com/alteryx/autonormalize>), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time.

It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.

This package implements the Bayesian Additive Voronoi Tessellation model for non-parametric regression and machine learning as introduced in Stone and Gosling (2025) <doi:10.1080/10618600.2024.2414104>. This package provides a flexible alternative to BART (Bayesian Additive Regression Trees) using Voronoi tessellations instead of trees. Users can fit Bayesian regression models, estimate posterior distributions, and visualise the resulting tessellations. It is particularly useful for spatial data analysis, machine learning regression, complex function approximation and Bayesian modeling where the underlying structure is unknown. The method is well-suited to capturing spatial patterns and non-linear relationships.

Estimate the lower and upper bound of asymptomatic cases in an epidemic using the capture/recapture methods from Böhning et al. (2020) <doi:10.1016/j.ijid.2020.06.009> and Rocchetti et al. (2020) <doi:10.1101/2020.07.14.20153445>. Note there is currently some discussion about the validity of the methods implemented in this package. You should read carefully the original articles, alongside this answer from Li et al. (2022) <doi:10.48550/arXiv.2209.11334> before using this package in your project.