Bayesian Multi-Plate High-Throughput Screening of Compounds

Can be used for joint identification of candidate compound hits from multiple assays, in drug discovery. This package implements the framework of I. D. Shterev, D. B. Dunson, C. Chan and G. D. Sempowski. "Bayesian Multi-Plate High-Throughput Screening of Compounds", Scientific Reports 8(1):9551, 2018. This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.

Distribution Functions and Parameter Estimates for the Triangle Distribution

Provides the "r, q, p, and d" distribution functions for the triangle distribution. Also includes maximum likelihood estimation of parameters.

Topic Models

Provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

Visualizations of Distributions and Uncertainty

Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) < https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) , quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) , complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) , and fit curves with multiple uncertainty ribbons.

Local Polynomial Density Estimation and Inference

Implements local polynomial distribution and density methods for point estimation, inference and bandwidth selection, documented in Cattaneo, Jansson and Ma (2020) , Cattaneo, Jansson and Ma (2022) , and Cattaneo, Jansson and Ma (2024) . lpdensity() constructs local polynomial distribution and density estimators with robust bias-corrected inference, and lpbwdensity() implements data-driven bandwidth selection.

High Performance Remote File System, Database and 'Geospatial' Access Using 'duckdb'

Provides friendly wrappers for creating 'duckdb'-backed connections to tabular datasets ('csv', parquet, etc) on local or remote file systems. This mimics the behaviour of "open_dataset" in the 'arrow' package, but in addition to 'S3' file system also generalizes to any list of 'http' URLs.

Tracking Data

Access and manipulate spatial tracking data, with straightforward coercion from and to other formats. Filter for speed and create time spent maps from tracking data. There are coercion methods to convert between 'trip' and 'ltraj' from 'adehabitatLT', and between 'trip' and 'psp' and 'ppp' from 'spatstat'. Trip objects can be created from raw or grouped data frames, and from types in the 'sp', sf', 'amt', 'trackeR', 'mousetrap', and other packages, Sumner, MD (2011) < https://figshare.utas.edu.au/articles/thesis/The_tag_location_problem/23209538>.

Fast 'Rcpp' Implementation of Gauss-Hermite Quadrature

Fast, numerically-stable Gauss-Hermite quadrature rules and utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A. (1994) for a reference on these methods.

R Package for Aqua Culture

Solves the individual bioenergetic balance for different aquaculture sea fish (Sea Bream and Sea Bass; Brigolin et al., 2014 ) and shellfish (Mussel and Clam; Brigolin et al., 2009 ; Solidoro et al., 2000 ). Allows for spatialized model runs and population simulations.

Identifying Stocks in Genetic Data

Provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) .