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.

This package provides functions to fit Accurate Generalized Linear Model (AGLM) models, visualize them, and predict for new data. AGLM is defined as a regularized GLM which applies a sort of feature transformations using a discretization of numerical features and specific coding methodologies of dummy variables. For more information on AGLM, see Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa (2020) <https://www.institutdesactuaires.com/global/gene/link.php?doc_id=16273&fg=1>.

This package provides a collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.

Perform the Adaptable Regularized Hotelling's T^2 test (ARHT) proposed by Li et al., (2016) <arXiv:1609.08725>. Both one-sample and two-sample mean test are available with various probabilistic alternative prior models. It contains a function to consistently estimate higher order moments of the population covariance spectral distribution using the spectral of the sample covariance matrix (Bai et al. (2010) <doi:10.1111/j.1467-842X.2010.00590.x>). In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately with a given correlation matrix when the degrees of freedom are large.

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.

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/>.

Self-Attention algorithm helper functions and demonstration vignettes of increasing depth on how to construct the Self-Attention algorithm, this is based on Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>, Dan Jurafsky and James H. Martin (2022, ISBN:978-0131873216) <https://web.stanford.edu/~jurafsky/slp3/> "Speech and Language Processing (3rd ed.)" and Alex Graves (2020) <https://www.youtube.com/watch?v=AIiwuClvH6k> "Attention and Memory in Deep Learning".

The ArcGIS Places service is a ready-to-use location service that can search for businesses and geographic locations around the world. It allows you to find, locate, and discover detailed information about each place. Query for places near a point, within a bounding box, filter based on categories, or provide search text. arcgisplaces integrates with sf for out of the box compatibility with other spatial libraries. Learn more in the Places service API reference <https://developers.arcgis.com/rest/places/>.

This package provides a toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>).

An isotope natural abundance correction algorithm that is needed especially for high resolution mass spectrometers. Supports correction for 13C, 2H and 15N. Su X, Lu W and Rabinowitz J (2017) <doi:10.1021/acs.analchem.7b00396>.

This package contains some tools for testing, analyzing time series data and fitting popular time series models such as ARIMA, Moving Average and Holt Winters, etc. Most functions also provide nice and clear outputs like SAS does, such as identify, estimate and forecast, which are the same statements in PROC ARIMA in SAS.

Allow user to run the Adaptive Correlated Spike and Slab (ACSS) algorithm, corresponding INdependent Spike and Slab (INSS) algorithm, and Giannone, Lenza and Primiceri (GLP) algorithm with adaptive burn-in. All of the three algorithms are used to fit high dimensional data set with either sparse structure, or dense structure with smaller contributions from all predictors. The state-of-the-art GLP algorithm is in Giannone, D., Lenza, M., & Primiceri, G. E. (2021, ISBN:978-92-899-4542-4) "Economic predictions with big data: The illusion of sparsity". The two new algorithms, ACSS algorithm and INSS algorithm, and the discussion on their performance can be seen in Yang, Z., Khare, K., & Michailidis, G. (2024, submitted to Journal of Business & Economic Statistics) "Bayesian methodology for adaptive sparsity and shrinkage in regression".

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.

This package provides tools for raster georeferencing, grid affine transforms, and general raster logic. These functions provide converters between raster specifications, world vector, geotransform, RasterIO window, and RasterIO window in sf package list format. There are functions to offset a matrix by padding any of four corners (useful for vectorizing neighbourhood operations), and helper functions to harvesting user clicks on a graphics device to use for simple georeferencing of images. Methods used are available from <https://en.wikipedia.org/wiki/World_file> and <https://gdal.org/user/raster_data_model.html>.

This package provides functions to convert origin-destination data, represented as straight desire lines in the sf Simple Features class system, into JSON files that can be directly imported into A/B Street <https://www.abstreet.org>, a free and open source tool for simulating urban transport systems and scenarios of change <doi:10.1007/s10109-020-00342-2>.

We provide a stage-wise selection method using genetic algorithm which can perform fast interaction selection in high-dimensional linear regression models with two-way interaction effects under strong, weak, or no heredity condition. Ye, C.,and Yang,Y. (2019) <doi:10.1109/TIT.2019.2913417>.

This package provides a set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <doi:10.1016/j.acags.2020.100026>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <doi:10.1007/BF00891269>) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages ALDEx2', edgeR and DESeq2 (Fernandes et al (2014) <doi:10.1186/2049-2618-2-15>, Anders et al. (2013)<doi:10.1038/nprot.2013.099>).

This package provides a toolkit for archaeological time series and time intervals. This package provides a system of classes and methods to represent and work with archaeological time series and time intervals. Dates are represented as "rata die" and can be converted to (virtually) any calendar defined by Reingold and Dershowitz (2018) <doi:10.1017/9781107415058>. This packages offers a simple API that can be used by other specialized packages.

For researchers to quickly and comprehensively acquire disease genes, so as to understand the mechanism of disease, we developed this program to acquire disease-related genes. The data is integrated from three public databases. The three databases are eDGAR', DrugBank and MalaCards'. The eDGAR is a comprehensive database, containing data on the relationship between disease and genes. DrugBank contains information on 13443 drugs and 5157 targets. MalaCards integrates human disease information, including disease-related genes.

The goal of andurinha is provide a fast and friendly way to process spectroscopic data. It is intended for processing several spectra of samples with similar composition (tens to hundreds of spectra). It compiles spectroscopy data files, produces standardized and second derivative spectra, finds peaks and allows to select the most significant ones based on the second derivative/absorbance sum spectrum. It also provides functions for graphic evaluation of the outputs.

The adapted pair correlation function transfers the concept of the pair correlation function from point patterns to patterns of objects of finite size and irregular shape (e.g. lakes within a country). The pair correlation function describes the spatial distribution of objects, e.g. random, aggregated or regularly spaced. This is a reimplementation of the method suggested by Nuske et al. (2009) <doi:10.1016/j.foreco.2009.09.050> using the library GEOS'.

With appRiori <doi:10.1177/25152459241293110>, users upload the research variables and the app guides them to the best set of comparisons fitting the hypotheses, for both main and interaction effects. Through a graphical explanation and empirical examples on reproducible data, it is shown that it is possible to understand both the logic behind the planned comparisons and the way to interpret them when a model is tested.

Helps enable adaptive management by codifying knowledge in the form of models generated from numerous analyses and data sets. Facilitates this process by storing all models and data sets in a single object that can be updated and saved, thus tracking changes in knowledge through time. A shiny application called AM Model Manager (modelMgr()) enables the use of these functions via a GUI.

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.