This package provides functions that facilitate the use of accepted taxonomic nomenclature, collection of functional trait data, and assignment of functional group classifications to phytoplankton species. Possible classifications include Morpho-functional group (MFG; Salmaso et al. 2015 <doi:10.1111/fwb.12520>) and CSR (Reynolds 1988; Functional morphology and the adaptive strategies of phytoplankton. In C.D. Sandgren (ed). Growth and reproductive strategies of freshwater phytoplankton, 388-433. Cambridge University Press, New York). Versions 2.0.0 and later includes new functions for querying the algaebase online taxonomic database (www.algaebase.org), however these functions require a valid API key that must be acquired from the algaebase administrators. Note that none of the algaeClassify authors are affiliated with algaebase in any way. Taxonomic names can also be checked against a variety of taxonomic databases using the Global Names Resolver service via its API (<https://resolver.globalnames.org/api>). In addition, currently accepted and outdated synonyms, and higher taxonomy, can be extracted for lists of species from the ITIS database via its JSON web service API. The algaeClassify package is a product of the GEISHA (Global Evaluation of the Impacts of Storms on freshwater Habitat and Structure of phytoplankton Assemblages), funded by CESAB (Centre for Synthesis and Analysis of Biodiversity) and the U.S. Geological Survey John Wesley Powell Center for Synthesis and Analysis, with data and other support provided by members of GLEON (Global Lake Ecology Observation Network). DISCLAIMER: This software has been approved for release by the U.S. Geological Survey (USGS). Although the software has been subjected to rigorous review, the USGS reserves the right to update the software as needed pursuant to further analysis and review. No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. Furthermore, the software is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.

Obtain overlapping clustering models for object-by-variable data matrices using the Additive Profile Clustering (ADPROCLUS) method. Also contains the low dimensional ADPROCLUS method for simultaneous dimension reduction and overlapping clustering. For reference see Depril, Van Mechelen, Mirkin (2008) <doi:10.1016/j.csda.2008.04.014> and Depril, Van Mechelen, Wilderjans (2012) <doi:10.1007/s00357-012-9112-5>.

Continuous and discrete (count or categorical) estimation of density, probability mass function (p.m.f.) and regression functions are performed using associated kernels. The cross-validation technique and the local Bayesian procedure are also implemented for bandwidth selection.

This package provides a user-friendly shiny application to explore statistical associations and visual patterns in multivariate datasets. The app provides interactive correlation networks, bivariate plots, and summary tables for different types of variables (numeric and categorical). It also supports optional survey weights and range-based filters on association strengths, making it suitable for the exploration of survey and public data by non-technical users, journalists, educators, and researchers. For background and methodological details, see Soetewey et al. (2025) <doi:10.1016/j.softx.2025.102483>.

This package contains tools to fit the additive hazards model to data from a cohort, random sampling, two-phase Bernoulli sampling and two-phase finite population sampling, as well as calibration tool to incorporate phase I auxiliary information into the two-phase data model fitting. This package provides regression parameter estimates and their model-based and robust standard errors. It also offers tools to make prediction of individual specific hazards.

Computing and visualizing comparative asymptotic timings of different algorithms and code versions. Also includes functionality for comparing empirical timings with expected references such as linear or quadratic, <https://en.wikipedia.org/wiki/Asymptotic_computational_complexity> Also includes functionality for measuring asymptotic memory and other quantities.

It computes two frequently applied actuarial measures, the expected shortfall and the value at risk. Seven well-known classical distributions in connection to the Bell generalized family are used as follows: Bell-exponential distribution, Bell-extended exponential distribution, Bell-Weibull distribution, Bell-extended Weibull distribution, Bell-Lomax distribution, Bell-Burr-12 distribution, and Bell-Burr-X distribution. Related works include: a) Fayomi, A., Tahir, M. H., Algarni, A., Imran, M., & Jamal, F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. <doi:10.1155/2022/2489998>. b) Alsadat, N., Imran, M., Tahir, M. H., Jamal, F., Ahmad, H., & Elgarhy, M. (2023). "Compounded Bell-G class of statistical models with applications to COVID-19 and actuarial data". Open Physics, 21(1), 20220242. <doi:10.1515/phys-2022-0242>.

Lets you open a fixed-width ASCII file (.txt or .dat) that has an accompanying setup file (.sps or .sas). These file combinations are sometimes referred to as .txt+.sps, .txt+.sas, .dat+.sps, or .dat+.sas. This will only run in a txt-sps or txt-sas pair in which the setup file contains instructions to open that text file. It will NOT open other text files, .sav, .sas, or .por data files. Fixed-width ASCII files with setup files are common in older (pre-2000) government data.

This package implements a multiple testing approach to the choice of a threshold gamma on the p-values using the Average Power Function (APF) and Bayes False Discovery Rate (FDR) robust estimation. Function apf_fdr() estimates both quantities from either raw data or p-values. Function apf_plot() produces smooth graphs and tables of the relevant results. Details of the methods can be found in Quatto P, Margaritella N, et al. (2019) <doi:10.1177/0962280219844288>.

This package implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.

The main application concerns to a new robust optimization package with two major contributions. The first contribution refers to the assessment of the adequacy of probabilistic models through a combination of several statistics, which measure the relative quality of statistical models for a given data set. The second one provides a general purpose optimization method based on meta-heuristics functions for maximizing or minimizing an arbitrary objective function.

Bindings to libarchive <http://www.libarchive.org> the Multi-format archive and compression library. Offers R connections and direct extraction for many archive formats including tar', ZIP', 7-zip', RAR', CAB and compression formats including gzip', bzip2', compress', lzma and xz'.

Presents a series of molecular and genetic routines in the R environment with the aim of assisting in analytical pipelines before and after the use of asreml or another library to perform analyses such as Genomic Selection or Genome-Wide Association Analyses. Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) <https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/ASRgenomics_Manual.pdf>.

Visualization of Design of Experiments from the agricolae package with ggplot2 framework The user provides an experiment design from the agricolae package, calls the corresponding function and will receive a visualization with ggplot2 based functions that are specific for each design. As there are many different designs, each design is tested on its type. The output can be modified with standard ggplot2 commands or with other packages with ggplot2 function extensions.

Imports Azure Application Insights for web pages into Shiny apps via Microsoft's JavaScript snippet. Allows app developers to submit page tracking and submit events.

Create beautiful and interactive visualizations in a single function call. The data.table package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation).

Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. AntAngioCOOL exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, AntAngioCOOL reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset.

Estimate and plot confounder-adjusted survival curves using either Direct Adjustment', Direct Adjustment with Pseudo-Values', various forms of Inverse Probability of Treatment Weighting', two forms of Augmented Inverse Probability of Treatment Weighting', Empirical Likelihood Estimation or Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times. Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) <doi:10.1002/sim.9681>.

This package provides a collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al <doi:10.1186/s12874-021-01450-3>.

NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) <doi:10.1007/s10618-005-0026-2>.

This package implements several tools that are used in animal social network analysis, as described in Whitehead (2007) Analyzing Animal Societies <University of Chicago Press> and Farine & Whitehead (2015) <doi: 10.1111/1365-2656.12418>. In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data.

This package provides functions for displaying multiple images or scatterplots with a color scale, i.e., heat maps, possibly with projected coordinates. The package relies on the base graphics system, so graphics are rendered rapidly.

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.

An ASCII ruler is for measuring text and is especially useful for sequence analysis. Included in this package are methods to create ASCII rulers and associated GenBank sequence blocks, multi-column text displays that make it easy for viewers to locate nucleotides by position.