Obtain network structures from animal GPS telemetry observations and statistically analyse them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics.

Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC.

This package performs AnchorRegression proposed by Rothenhäusler et al. 2020. The code is adapted from the original paper repository. (<https://github.com/rothenhaeusler/anchor-regression>) The code was developed independently from the authors of the paper.

This package provides a high-performance, flexible and extensible framework to develop continuous-time agent based models. Its high performance allows it to simulate millions of agents efficiently. Agents are defined by their states (arbitrary R lists). The events are handled in chronological order. This avoids the multi-event interaction problem in a time step of discrete-time simulations, and gives precise outcomes. The states are modified by provided or user-defined events. The framework provides a flexible and customizable implementation of state transitions (either spontaneous or caused by agent interactions), making the framework suitable to apply to epidemiology and ecology, e.g., to model life history stages, competition and cooperation, and disease and information spread. The agent interactions are flexible and extensible. The framework provides random mixing and network interactions, and supports multi-level mixing patterns. It can be easily extended to other interactions such as inter- and intra-households (or workplaces and schools) by subclassing an R6 class. It can be used to study the effect of age-specific, group-specific, and contact- specific intervention strategies, and complex interactions between individual behavior and population dynamics. This modeling concept can also be used in business, economical and political models. As a generic event based framework, it can be applied to many other fields. More information about the implementation and examples can be found at <https://github.com/junlingm/ABM>.

Estimate diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where can not measure the golden standard but can estimate it using the attributable fraction.

Estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent response and covariates are mismatched and observed intermittently within subjects. Kernel weighted estimating equations are used for generalized linear models with either time-invariant or time-dependent coefficients. Cao, H., Li, J., and Fine, J. P. (2016) <doi:10.1214/16-EJS1141>. Cao, H., Zeng, D., and Fine, J. P. (2015) <doi:10.1111/rssb.12086>.

This package contains functions to help create an Analysis Results Dataset. The dataset follows industry recommended structure. The dataset can be created in multiple passes, using different data frames as input. Analysis Results Datasets are used in the pharmaceutical and biotech industries to capture analysis in a common tabular data structure.

The Genetic Algorithm (GA) is a type of optimization method of Evolutionary Algorithms. It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. The hybrid GAs combine the exploration strength of a stochastic GAs with the exact convergence ability of any type of deterministic local search algorithms such as simulated-annealing, in addition to other nature-inspired algorithms such as ant colony optimization, particle swarm optimization etc. The package adana includes a rich working environment with its many functions that make possible to build and work regular GA, adaptive GA, hybrid GA and hybrid adaptive GA for any kind of optimization problems. Cebeci, Z. (2021, ISBN: 9786254397448).

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.

This package provides functions for interacting directly with the ALTADATA API. With this R package, developers can build applications around the ALTADATA API without having to deal with accessing and managing requests and responses. ALTADATA is a curated data marketplace for more information go to <https://www.altadata.io>.

Covers several areas of data processing: batch-splitting, reading and writing of large data files, data tiling, one-hot encoding and decoding of data tiles, stratified proportional (random or probabilistic) data sampling, data normalization and thresholding, substring location and commonalities inside strings, and location and tabulation of amino acids, modifications or associated monoisotopic masses inside modified peptides. The extractor utility implements code from Matrix.utils', Varrichio C (2020), <https://cran.r-project.org/package=Matrix.utils>.

This package provides methods (<doi:10.7717/peerj.11534>) are provided of calibrating and predicting shifts in allele frequencies through redundancy analysis ('vegan::rda()') and generalized additive models ('mgcv::gam()'). Visualization functions for predicted changes in allele frequencies include shift.dot.ggplot()', shift.pie.ggplot()', shift.moon.ggplot()', shift.waffle.ggplot() and shift.surf.ggplot() that are made with input data sets that are prepared by helper functions for each visualization method. Examples in the documentation show how to prepare animated climate change graphics through a time series with the gganimate package. Function amova.rda() shows how Analysis of Molecular Variance can be directly conducted with the results from redundancy analysis.

This package provides a set of dynamic measurement models to estimate latent vote shares from noisy polling sources. The models build on Jackman (2009, ISBN: 9780470011546) and feature specialized methods for bias adjustment based on past performance and correction for asymmetric errors based on candidate political alignment.

Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.

Streamline use of the All of Us Researcher Workbench (<https://www.researchallofus.org/data-tools/workbench/>)with tools to extract and manipulate data from the All of Us database. Increase interoperability with the Observational Health Data Science and Informatics ('OHDSI') tool stack by decreasing reliance of All of Us tools and allowing for cohort creation via Atlas'. Improve reproducible and transparent research using All of Us'.

Simple and transparent parsing of genotype/dosage data from an input Variant Call Format (VCF) file, matching of genotype coordinates to the component Single Nucleotide Polymorphisms (SNPs) of an existing polygenic score (PGS), and application of SNP weights to dosages for the calculation of a polygenic score for each individual in accordance with the additive weighted sum of dosages model. Methods are designed in reference to best practices described by Collister, Liu, and Clifton (2022) <doi:10.3389/fgene.2022.818574>.

This package provides WHO Child Growth Standards (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: <https://www.who.int/tools/child-growth-standards>.

This package provides tools for analysing the geometry of configurations in high-dimensional spaces using the Average Membership Degree (AMD) framework and synthetic configuration generation. The package supports a domain-agnostic approach to studying the shape, dispersion, and internal structure of point clouds, with applications across biological and ecological datasets, including those derived from deep-time records. The AMD framework builds on the idea that strongly coupled systems may occupy a limited set of recurrent regimes in state space, producing high-occupancy regions separated by sparsely populated transitional configurations. The package focuses on detecting these concentration patterns and quantifying their geometric definition without assuming any underlying dynamical model. It provides AMD curve computation, cluster assignment, and sigma-equivalent estimation, together with S3 methods for plotting, printing, and summarising AMD and sigma-equivalent objects. Mendoza (2025) <https://mmendoza1967.github.io/AMDconfigurations/>.

Retrieve Amazon EC2 instance metadata from within the running instance.

Fast generators and iterators for permutations, combinations, integer partitions and compositions. The arrangements are in lexicographical order and generated iteratively in a memory efficient manner. It has been demonstrated that arrangements outperforms most existing packages of similar kind. Benchmarks could be found at <https://randy3k.github.io/arrangements/articles/benchmark.html>.

In panel data settings, specifies set of candidate models, fits them to data from pre-treatment validation periods, and selects model as average over candidate models, weighting each by posterior probability of being most robust given its differential average prediction errors in pre-treatment validation periods. Subsequent estimation and inference of causal effect's bounds accounts for both model and sampling uncertainty, and calculates the robustness changepoint value at which bounds go from excluding to including 0. The package also includes a range of diagnostic plots, such as those illustrating models differential average prediction errors and the posterior distribution of which model is most robust.

This package provides a dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published MATLAB code by Hay et al. (2019) <doi:10.1130/G46019.1>. The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review).

Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology.

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.