This package provides functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or NIFTI format. Note that the latest version of XQuartz seems to be necessary under MacOS.

The Langmuir and Freundlich adsorption isotherms are pivotal in characterizing adsorption processes, essential across various scientific disciplines. Proper interpretation of adsorption isotherms involves robust fitting of data to the models, accurate estimation of parameters, and efficiency evaluation of the models, both in linear and non-linear forms. For researchers and practitioners in the fields of chemistry, environmental science, soil science, and engineering, a comprehensive package that satisfies all these requirements would be ideal for accurate and efficient analysis of adsorption data, precise model selection and validation for rigorous scientific inquiry and real-world applications. Details can be found in Langmuir (1918) <doi:10.1021/ja02242a004> and Giles (1973) <doi:10.1111/j.1478-4408.1973.tb03158.x>.

Data sets are referred to in the text "Applied Survival Analysis Using R" by Dirk F. Moore, Springer, 2016, ISBN: 978-3-319-31243-9, <DOI:10.1007/978-3-319-31245-3>.

This package provides a summarization method to estimate allele-specific copy number signals for Affymetrix SNP microarrays using non-negative matrix factorization (NMF).

The company, Algorithmia, houses the largest marketplace of online algorithms. This package essentially holds a bunch of REST wrappers that make it very easy to call algorithms in the Algorithmia platform and access files and directories in the Algorithmia data API. To learn more about the services they offer and the algorithms in the platform visit <http://algorithmia.com>. More information for developers can be found at <https://algorithmia.com/developers>.

This package contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.

This package provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. Proposed by von Neumann J. (1951), <https://mcnp.lanl.gov/pdf_files/>, the function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) <https://www.jstor.org/stable/4356322>, NEAL, Radford M. (2003) <https://www.jstor.org/stable/3448413> and Bishop, Christopher M. (2006, ISBN: 978-0387310732).

ACE (Advanced Cohort Engine) is a powerful tool that allows constructing cohorts of patients extremely quickly and efficiently. This package is designed to interface directly with an instance of ACE search engine and facilitates API queries and data dumps. Prerequisite is a good knowledge of the temporal language to be able to efficiently construct a query. More information available at <https://shahlab.stanford.edu/start>.

This package provides a collection of model checking methods for semiparametric accelerated failure time (AFT) models under the rank-based approach. For the (computational) efficiency, Gehan's weight is used. It provides functions to verify whether the observed data fit the specific model assumptions such as a functional form of each covariate, a link function, and an omnibus test. The p-value offered in this package is based on the Kolmogorov-type supremum test and the variance of the proposed test statistics is estimated through the re-sampling method. Furthermore, a graphical technique to compare the shape of the observed residual to a number of the approximated realizations is provided. See the following references; A general model-checking procedure for semiparametric accelerated failure time models, Statistics and Computing, 34 (3), 117 <doi:10.1007/s11222-024-10431-7>; Diagnostics for semiparametric accelerated failure time models with R package afttest', arXiv, <doi:10.48550/arXiv.2511.09823>.

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 package provides functions to accompany the book "Applied Statistical Modeling for Ecologists" by Marc Kéry and Kenneth F. Kellner (2024, ISBN: 9780443137150). Included are functions for simulating and customizing the datasets used for the example models in each chapter, summarizing output from model fitting engines, and running custom Markov Chain Monte Carlo.

Set of tools for statistical analysis, visualization, and reporting of agroindustrial and agricultural experiments. The package provides functions to perform ANOVA with post-hoc tests (e.g. Tukey HSD and Duncan MRR), compute coefficients of variation, and generate publication-ready summaries. High-level wrappers allow automated multi-variable analysis with optional clustering by experimental factors, as well as direct export of results to Excel spreadsheets and high-resolution image tables for reporting. Functions build on ggplot2', stats', and related packages and follow methods widely used in agronomy (field trials and plant breeding). Key references include Tukey (1949) <doi:10.2307/3001913>, Duncan (1955) <doi:10.2307/3001478>, and Cohen (1988, ISBN:9781138892899); see also agricolae <https://CRAN.R-project.org/package=agricolae> and Wickham (2016, ISBN:9783319242750> for ggplot2'. Versión en español: Conjunto de herramientas para el análisis estadà stico, visualización y generación de reportes en ensayos agroindustriales y agrà colas. Incluye funciones para ANOVA con pruebas post-hoc, resúmenes automáticos multivariables con o sin agrupamiento por factores, y exportación directa de resultados a Excel e imágenes de alta resolución para informes técnicos.

Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.

An interactive shiny application for performing non-compartmental analysis (NCA) on pre-clinical and clinical pharmacokinetic data. The package builds on PKNCA for core estimators and provides interactive visualizations, CDISC outputs ('ADNCA', PP', ADPP') and configurable TLGs (tables, listings, and graphs). Typical use cases include exploratory analysis, validation, reporting or teaching/demonstration of NCA methods. Methods and core estimators are described in Denney, Duvvuri, and Buckeridge (2015) "Simple, Automatic Noncompartmental Analysis: The PKNCA R Package" <doi:10.1007/s10928-015-9432-2>.

Facilitate the analysis of data related to aquatic ecology, specifically the establishment of carbon budget. Currently, the package allows the below analysis. (i) the calculation of greenhouse gas flux based on data obtained from trace gas analyzer using the method described in Lin et al. (2024). (ii) the calculation of Dissolved Oxygen (DO) metabolism based on data obtained from dissolved oxygen data logger using the method described in Staehr et al. (2010). Yong et al. (2024) <doi:10.5194/bg-21-5247-2024>. Staehr et al. (2010) <doi:10.4319/lom.2010.8.0628>.

This package provides a unified and straightforward interface for performing a variety of meta-analysis methods directly from user data. Users can input a data frame, specify key parameters, and effortlessly execute and compare multiple common meta-analytic models. Designed for immediate usability, the package facilitates transparent, reproducible research without manual implementation of each analytical method. Ideal for researchers aiming for efficiency and reproducibility, it streamlines workflows from data preparation to results interpretation.

Calculate the area of triangles and polygons using the shoelace formula. Area may be signed, taking into account path orientation, or unsigned, ignoring path orientation. The shoelace formula is described at <https://en.wikipedia.org/wiki/Shoelace_formula>.

Interface package for sala', the spatial network analysis library from the depthmapX software application. The R parts of the code are based on the rdepthmap package. Allows for the analysis of urban and building-scale networks and provides metrics and methods usually found within the Space Syntax domain. Methods in this package are described by K. Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax methodology", and also by A. Turner (2004) <https://discovery.ucl.ac.uk/id/eprint/2651> "Depthmap 4: a researcher's handbook".

Flagger to detect acute kidney injury (AKI) in a patient dataset.

Fast processing of ArcGIS FeatureCollection protocol buffers in R. It is designed to work seamlessly with httr2 and integrates with sf'.

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>).

This package provides a collection of tools for the analysis of habitat selection.

This package provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmottâ s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202> and Garai et al. (2024) <doi:10.1007/s11063-024-11552-w>.

Connect to the Adobe Analytics API v2.0 <https://github.com/AdobeDocs/analytics-2.0-apis> which powers Analysis Workspace'. The package was developed with the analyst in mind, and it will continue to be developed with the guiding principles of iterative, repeatable, timely analysis.