Easy-to-use tools for performing complex queries on avidaDB', a semantic database that stores genomic and transcriptomic data of self-replicating computer programs (known as digital organisms) that mutate and evolve within a user-defined computational environment.

An implementation of the additive polynomial (AP) design matrix. It constructs and appends an AP design matrix to a data frame for use with longitudinal data subject to seasonality.

Convert several png files into an animated png file. This package exports only a single function `apng'. Call the apng function with a vector of file names (which should be png files) to convert them to a single animated png file.

Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 <doi:10.1667/RR13729.1>) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 <doi:10.1201/9781420010138>), extended regression calibration (Little et al. 2023 <doi:10.1038/s41598-023-42283-y>), Monte Carlo maximum likelihood (Stayner et al. 2007 <doi:10.1667/RR0677.1>), frequentist model averaging (Kwon et al. 2023 <doi:10.1371/journal.pone.0290498>), and Bayesian model averaging (Kwon et al. 2016 <doi:10.1002/sim.6635>). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic.

Fit various smoothing spline models. Includes an ssr() function for smoothing spline regression, an nnr() function for nonparametric nonlinear regression, an snr() function for semiparametric nonlinear regression, an slm() function for semiparametric linear mixed-effects models, and an snm() function for semiparametric nonlinear mixed-effects models. See Wang (2011) <doi:10.1201/b10954> for an overview.

Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated intervals and estimates partial autocorrelations using Durbin-Levinson. This package calculates the autocorrelation power spectrum, computes cross-correlations between two time series, computes bandwidth for any time series, and performs autocorrelation frequency analysis. It also calculates the periodicity of a time series.

This package provides access to the species checklist published in List of the Birds of Peru by Plenge, M. A. and Angulo, F. (version 23-03-2026) <https://sites.google.com/site/boletinunop/checklist>. The package exposes the current Peru bird checklist as an R dataset and includes tools for species lookup, taxonomic reconciliation, and fuzzy matching of scientific names. These features help streamline taxonomic validation for researchers and conservationists.

Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) <doi:10.1111/2041-210X.12229> as described in Ma, C. et al. (2020) <doi:10.1111/2041-210X.13426>.

These dataset contains daily quality air measurements in Spain over a period of 18 years (from 2001 to 2018). The measurements refer to several pollutants. These data are openly published by the Government of Spain. The datasets were originally spread over a number of files and formats. Here, the same information is contained in simple dataframe for convenience of researches, journalists or general public. See the Spanish Government website <http://www.miteco.gob.es/> for more information.

Made to make your life simpler with packages, by installing and loading a list of packages, whether they are on CRAN, Bioconductor or github. For github, if you do not have the full path, with the maintainer name in it (e.g. "achateigner/topReviGO"), it will be able to load it but not to install it.

Manage storage in Microsoft's Azure cloud: <https://azure.microsoft.com/en-us/products/category/storage/>. On the admin side, AzureStor includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the AzureR family of packages.

Build and train a variational autoencoder (VAE) for mixed-type tabular data (continuous, binary, categorical). Models are implemented using TensorFlow and Keras via the reticulate interface, enabling reproducible VAE training for heterogeneous tabular datasets.

Wraps the AT Protocol (Authenticated Transfer Protocol) behind Bluesky <https://bsky.social>. Functions can be used for, among others, retrieving posts and followers from the network or posting content.

Actuarial reports are prepared for the last day of a specific period, such as a month, a quarter or a year. Actuarial models assume that certain events happen at the beginning or end of periods. The package contains functions to easily refer to the first or last (working) day within a specific period relative to a base date to facilitate actuarial reporting and to compare results.

Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.

This package provides functions to process minute level actigraphy-measured activity counts data and extract commonly used physical activity volume and fragmentation metrics.

Collect your data on digital marketing campaigns from Amazon S3 using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package provides a toolbox to read all R files inside a package and automatically generate @importFrom roxygen2 tags in the right place. Includes a shiny application to review the changes before applying them.

Experience studies are used by actuaries to explore historical experience across blocks of business and to inform assumption setting activities. This package provides functions for preparing data, creating studies, visualizing results, and beginning assumption development. Experience study methods, including exposure calculations, are described in: Atkinson & McGarry (2016) "Experience Study Calculations" <https://www.soa.org/49378a/globalassets/assets/files/research/experience-study-calculations.pdf>. The limited fluctuation credibility method used by the exp_stats() function is described in: Herzog (1999, ISBN:1-56698-374-6) "Introduction to Credibility Theory".

Supplies a set of functions to query air travel data for user- specified years and airports. Datasets include on-time flights, airlines, airports, planes, and weather.

An R Shiny application for visual and statistical exploration and web communication of archaeological spatial data, either remains or sites. It offers interactive 3D and 2D visualisations (cross sections and maps of remains, timeline of the work made in a site) which can be exported in SVG and HTML formats. It performs simple spatial statistics (convex hull, regression surfaces, 2D kernel density estimation) and allows exporting data to other online applications for more complex methods. archeoViz can be used offline locally or deployed on a server, either with interactive input of data or with a static data set. Example is provided at <https://analytics.huma-num.fr/archeoviz/en>.

This package provides tools for classical parameter estimation of adsorption isotherm models, including both linear and nonlinear forms of the Freundlich, Langmuir, and Temkin isotherms. This package allows users to fit these models to experimental data, providing parameter estimates along with fit statistics such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Error metrics are computed to evaluate model performance, and the package produces model fit plots with bootstrapped 95% confidence intervals. Additionally, it generates residual plots for diagnostic assessment of the models. Researchers and engineers in material science, environmental engineering, and chemical engineering can rigorously analyze adsorption behavior in their systems using this straightforward, non-Bayesian approach. For more details, see Harding (1907) <doi:10.2307/2987516>.

Amyloid propensity prediction neural network (APPNN) is an amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation.

Bland-Altman plot and scatter plot with identity line for visualization and point and interval estimates for different metrics related to reproducibility/repeatability/agreement including the concordance correlation coefficient, intraclass correlation coefficient, within-subject coefficient of variation, smallest detectable difference, and mean normalized smallest detectable difference.