An interface to Azure Computer Vision <https://docs.microsoft.com/azure/cognitive-services/Computer-vision/Home> and Azure Custom Vision <https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/home>, building on the low-level functionality provided by the AzureCognitive package. These services allow users to leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of their own. Part of the AzureR family of packages.

The efficient Markov chain Monte Carlo estimation of stochastic volatility models with and without leverage (asymmetric and symmetric stochastic volatility models). Further, it computes the logarithm of the likelihood given parameters using particle filters.

The at-Risk (aR) approach is based on a two-step parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the aR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al. (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.

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>.

This contains helpful functions for parsing, managing, plotting, and visualizing activities, most often from GPX (GPS Exchange Format) files recorded by GPS devices. It allows easy parsing of the source files into standard R data formats, along with functions to compute derived data for the activity, and to plot the activity in a variety of ways.

An R API providing access to a relational database with macroeconomic data for Africa. The database contains >700 macroeconomic time series from mostly international sources, grouped into 50 macroeconomic and development-related topics. Series are carefully selected on the basis of data coverage for Africa, frequency, and relevance to the macro-development context. The project is part of the Kiel Institute Africa Initiative <https://www.ifw-kiel.de/institute/initiatives/kiel-institute-africa-initiative/>, which, amongst other things, aims to develop a parsimonious database with highly relevant indicators to monitor macroeconomic developments in Africa, accessible through a fast API and a web-based platform at <https://africamonitor.ifw-kiel.de/>. The database is maintained at the Kiel Institute for the World Economy <https://www.ifw-kiel.de/>.

This package implements persistent row and column annotations for R matrices. The annotations associated with rows and columns are preserved after subsetting, transposition, and various other matrix-specific operations. Intended use case is for storing and manipulating genomic datasets which typically consist of a matrix of measurements (like gene expression values) as well as annotations about rows (i.e. genomic locations) and annotations about columns (i.e. meta-data about collected samples). But annmatrix objects are also expected to be useful in various other contexts.

Simulates, fits, and predicts long-memory and anti-persistent time series, possibly mixed with ARMA, regression, transfer-function components. Exact methods (MLE, forecasting, simulation) are used. Bug reports should be done via GitHub (at <https://github.com/JQVeenstra/arfima>), where the development version of this package lives; it can be installed using devtools.

It performs Canonical Correlation Analysis and provides inferential guaranties on the correlation components. The p-values are computed following the resampling method developed in Winkler, A. M., Renaud, O., Smith, S. M., & Nichols, T. E. (2020). Permutation inference for canonical correlation analysis. NeuroImage, <doi:10.1016/j.neuroimage.2020.117065>. Furthermore, it provides plotting tools to visualize the results.

This package provides a function for estimating factor models. Give factor-adjusted statistics, factor-adjusted mean estimation (one-sample test) or factor-adjusted mean difference estimation (two-sample test).

Utility functions to download and process data produced by the ALARM Project, including 2020 redistricting files Kenny and McCartan (2021) <https://alarm-redist.org/posts/2021-08-10-census-2020/> and the 50-State Redistricting Simulations of McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) <doi:10.7910/DVN/SLCD3E>. The package extends the data introduced in McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) <doi:10.1038/s41597-022-01808-2> to also include states with only a single district. The package also includes the Japanese 2022 redistricting files from the 47-Prefecture Redistricting Simulations of Miyazaki, Yamada, Yatsuhashi, and Imai (2022) <doi:10.7910/DVN/Z9UKSH>.

Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample.

This package provides new_partialised() and new_composed(), which extend partial() and compose() functions of purrr to make it easier to extract and replace arguments and functions. It also has additional adverbial functions.

This package provides a number of functions to create and analyze factorial plans according to the Design of Experiments (DoE) approach, with the addition of some utility function to perform some statistical analyses. DoE approach follows the approach in "Design and Analysis of Experiments" by Douglas C. Montgomery (2019, ISBN:978-1-119-49244-3). The package also provides utilities used in the course "Analysis of Data and Statistics" at the University of Trento, Italy.

Comprehensive set of tools for performing system identification of both linear and nonlinear dynamical systems directly from data. The Automatic Regression for Governing Equations (ARGOS) simplifies the complex task of constructing mathematical models of dynamical systems from observed input and output data, supporting various types of systems, including those described by ordinary differential equations. It employs optimal numerical derivatives for enhanced accuracy and employs formal variable selection techniques to help identify the most relevant variables, thereby enabling the development of predictive models for system behavior analysis.

Import, manipulate and explore results generated by Antares', a powerful open source software developed by RTE (Réseau de Transport dâ à lectricité) to simulate and study electric power systems (more information about Antares here : <https://antares-simulator.org/>).

Facilitates plotting audiometric data (mostly) by preparing the coordinate system according to standards, given e. g. in American Speech-Language-Hearing Association (2005), <doi:10.1044/policy.GL2005-00014>.

Many complex plots are actually composite plots, such as oncoplot', funkyheatmap', upsetplot', etc. We can produce subplots using ggplot2 and combine them to create composite plots using aplot'. In this way, it is easy to customize these complex plots, by adding, deleting or modifying subplots in the final plot. This package provides a set of utilities to help users to create subplots and complex plots.

Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package reimplements the algorithms for calculating ALE data and develops highly interpretable visualizations for plotting these ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. â Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).â arXiv. <doi:10.48550/arXiv.2310.09877>.

This package provides functions for calculating the acute chronic workload ratio using three different methods: exponentially weighted moving average (EWMA), rolling average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this methods can be found in Williams et al. (2017) <doi:10.1136/bjsports-2016-096589> for EWMA and Windt & Gabbet (2018) for RAC and RAU <doi: 10.1136/bjsports-2017-098925>.

This package provides functions are provided to read and convert AIFF audio files to WAVE (WAV) format. This supports, for example, use of the tuneR package, which does not currently handle AIFF files. The AIFF file format is defined in <https://web.archive.org/web/20080125221040/http://www.borg.com/~jglatt/tech/aiff.htm> and <https://www.mmsp.ece.mcgill.ca/Documents/AudioFormats/AIFF/Docs/AIFF-1.3.pdf> .

The Aligned Corpus Toolkit (act) is designed for linguists that work with time aligned transcription data. It offers functions to import and export various annotation file formats ('ELAN .eaf, EXMARaLDA .exb and Praat .TextGrid files), create print transcripts in the style of conversation analysis, search transcripts (span searches across multiple annotations, search in normalized annotations, make concordances etc.), export and re-import search results (.csv and Excel .xlsx format), create cuts for the search results (print transcripts, audio/video cuts using FFmpeg and video sub titles in Subrib title .srt format), modify the data in a corpus (search/replace, delete, filter etc.), interact with Praat using Praat'-scripts, and exchange data with the rPraat package. The package is itself written in R and may be expanded by other users.

Implementation of a hybrid MCDM method build from the AHP (Analytic Hierarchy Process) and TOPSIS-2N (Technique for Order of Preference by Similarity to Ideal Solution - with two normalizations). This method is described in Souza et al. (2018) <doi: 10.1142/S0219622018500207>.

This package provides tools for constructing a matched design with multiple comparison groups. Further specifications of refined covariate balance restriction and exact match on covariate can be imposed. Matches are approximately optimal in the sense that the cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992) <doi:10.1016/0377-2217(92)90078-N>, Karmakar, Small and Rosenbaum (2019) <doi:10.1080/10618600.2019.1584900>.