Bayesian inference using the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) <https://www.jmlr.org/papers/v15/hoffman14a.html>. Designed for AD Model Builder ('ADMB') models, or when R functions for log-density and log-density gradient are available, such as Template Model Builder models and other special cases. Functionality is similar to Stan', and the rstan and shinystan packages are used for diagnostics and inference.

This package provides functions for the analysis of ant communities, aiming to standardize workflows in myrmecology. The package automates the assignment of species to functional guilds based on trophic strategies, feeding habits, and foraging behavior, using established classification frameworks (Silva et al., 2015 <doi:10.7476/9788574554419>; Silvestre et al., 2003 <isbn:9588151236>; Delabie et al., 2000 <https://www.researchgate.net/publication/44961742_Sampling_Ground-Dwelling_Ants_Case_Studies_from_the_World%27s_Rain_Forests>), and also includes a novel classification system implemented within the package, developed from ant species occurring in urban environments. It also includes routines to flag exotic species of Brazil (Vieira, 2025, unpublished master's thesis), identify endemic species (Silva et al., 2025 <doi:10.37885/250920259>), and classify species rarity and rarity forms of the Atlantic Forest (Silva et al., 2024 <doi:10.1016/j.biocon.2024.110640>). The package reduces manual effort and improves reproducibility, supporting research and biodiversity management of Neotropical ant communities.

This package implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) <doi:10.1002/sam.11586>, Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) <doi:10.1080/10618600.2021.1987257>, Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) <doi:10.1214/16-BA998>. These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) <doi:10.18637/jss.v110.i01>.

Computation of the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object.

This package provides a developer-facing interface to the Arrow Database Connectivity ('ADBC') SQLite driver for the purposes of building high-level database interfaces for users. ADBC <https://arrow.apache.org/adbc/> is an API standard for database access libraries that uses Arrow for result sets and query parameters.

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".

Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.

Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available.

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.

It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature, it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis. The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) <doi:10.1016/j.neuroimage.2018.07.060>.

This package provides novel nonparametric tests, APCSSA and APCSSM', for interaction in two-way ANOVA designs with balanced replications using all possible comparisons. These statistics extend previous methods, allow greater flexibility, and demonstrate higher power in detecting interactions for non-normal data. The package includes optimized functions for computing these test statistics, generating interaction plots, and simulating their null distributions. The companion package APCinteractionData is available on GitHub <https://github.com/tranbaokhue/APCinteractionData>. Methods are described and compared empirically in Tran, Wagaman, Nguyen, Jacobson, and Hartlaub (2024) <doi:10.48550/arXiv.2410.04700>.

The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylanâ s PhD dissertation.

Use Monte-Carlo and K-fold cross-validation coupled with machine- learning classification algorithms to perform population assignment, with functionalities of evaluating discriminatory power of independent training samples, identifying informative loci, reducing data dimensionality for genomic data, integrating genetic and non-genetic data, and visualizing results.

Estimate ideal efficiencies of aerosol sampling through sample lines. Functions were developed consistent with the approach described in Hogue, Mark; Thompson, Martha; Farfan, Eduardo; Hadlock, Dennis, (2014), "Hand Calculations for Transport of Radioactive Aerosols through Sampling Systems" Health Phys 106, 5, S78-S87, <doi:10.1097/HP.0000000000000092>.

The process of resolving and updating taxon names is necessary when working with biodiversity data. APCalign uses the Australian Plant Census (APC) and the Australian Plant Name Index (APNI) to align and update plant taxon names to current, accepted standards. APCalign also supplies information about the establishment status (i.e. native or introduced) of plant taxa across different states/territories.

This package provides a simple method to improve the accessibility of rmarkdown documents. The package provides functions for creating or modifying rmarkdown documents, resolving known errors and alerts that result in accessibility issues for screen reader users.

This package provides optimal combinations of input nodes and hidden neurons for fitting feedforward single-layer artificial neural networks in time series forecasting. Models are evaluated using root mean square error, mean absolute percentage error, and mean absolute error measures.

This package provides functions to model and decompose time series into principal components using singular spectrum analysis (de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>; de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>).

Estimate age-depth models from stratigraphic and sedimentological data, and transform data between the time and stratigraphic domain.

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.

Developed for Computing the probability density function, cumulative distribution function, random generation, estimating the parameters of asymmetric exponential power distribution, and robust regression analysis with error term that follows asymmetric exponential power distribution. The asymmetric exponential power distribution studied here is a special case of that introduced by Dongming and Zinde-Walsh (2009) <doi:10.1016/j.jeconom.2008.09.038>.

Given the parameters of a distribution, the package uses the concept of alpha-outliers by Davies and Gather (1993) to flag outliers in a data set. See Davies, L.; Gather, U. (1993): The identification of multiple outliers, JASA, 88 423, 782-792, <doi:10.1080/01621459.1993.10476339> for details.

We propose an age-dependent topic modelling (ATM) model, providing a low-rank representation of longitudinal records of hundreds of distinct diseases in large electronic health record data sets. The model assigns to each individual topic weights for several disease topics; each disease topic reflects a set of diseases that tend to co-occur as a function of age, quantified by age-dependent topic loadings for each disease. The model assumes that for each disease diagnosis, a topic is sampled based on the individualâ s topic weights (which sum to 1 across topics, for a given individual), and a disease is sampled based on the individualâ s age and the age-dependent topic loadings (which sum to 1 across diseases, for a given topic at a given age). The model generalises the Latent Dirichlet Allocation (LDA) model by allowing topic loadings for each topic to vary with age. References: Jiang (2023) <doi:10.1038/s41588-023-01522-8>.

Estimate group aggregates, where one can set user-defined conditions that each group of records must satisfy to be suitable for aggregation. If a group of records is not suitable, it is expanded using a collapsing scheme defined by the user. A paper on this package was published in the Journal of Statistical Software <doi:10.18637/jss.v112.i04>.