Extracts plain text from Rich Text Format (RTF) file.

Mappings for estimated one rep max from commonly used formulas. Convenience functions for turning mass/rep/set data into useful derived quantities.

Stores the data associated with your amplicon sequence analysis. This includes nucleotide sequences, abundance, sample and treatment assignments, taxonomic classifications, asv, otu and phylotype clusters, metadata, trees and various reports. It is designed to facilitate data analysis across multiple R packages with utility functions to read / write from mothur', qiime2', dada2', and phyloseq'.

The STRINGdb package provides an R interface to the STRING protein-protein interactions database. STRING is a database of known and predicted protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations. Each interaction is associated with a combined confidence score that integrates the various evidences.

Variants of strategy estimation (Dal Bo & Frechette, 2011, <doi:10.1257/aer.101.1.411>), including the model with parameters for the choice probabilities of the strategies (Breitmoser, 2015, <doi:10.1257/aer.20130675>), and the model with individual level covariates for the selection of strategies by individuals (Dvorak & Fehrler, 2018, <doi:10.2139/ssrn.2986445>).

This package provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.

The main function is icweib(), which fits a stratified Weibull proportional hazards model for left censored, right censored, interval censored, and non-censored survival data. We parameterize the Weibull regression model so that it allows a stratum-specific baseline hazard function, but where the effects of other covariates are assumed to be constant across strata. Please refer to Xiangdong Gu, David Shapiro, Michael D. Hughes and Raji Balasubramanian (2014) <doi:10.32614/RJ-2014-003> for more details.

Traditional model evaluation metrics fail to capture model performance under less than ideal conditions. This package employs techniques to evaluate models "under-stress". This includes testing models extrapolation ability, or testing accuracy on specific sub-samples of the overall model space. Details describing stress-testing methods in this package are provided in Haycock (2023) <doi:10.26076/2am5-9f67>. The other primary contribution of this package is provided to R users access to the Python library PyCaret <https://pycaret.org/> for quick and easy access to auto-tuned machine learning models.

The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation.

This package performs multiple testing corrections that take specific structure of hypotheses into account, as described in Sankaran & Holmes (2014) <doi:10.18637/jss.v059.i13>.

Quantify stratigraphic disorder using the metrics defined by Burgess (2016) <doi:10.2110/jsr.2016.10>. Contains a range of utility tools to construct and manipulate stratigraphic columns.

Tree-structured modelling of categorical predictors (Tutz and Berger (2018), <doi:10.1007/s11634-017-0298-6>) or measurement units (Berger and Tutz (2018), <doi:10.1080/10618600.2017.1371030>).

Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604).

This package provides exact analytical algorithms for computing optimum sample allocations in stratified sampling. Supports classical Neyman-Tschuprow allocation, minimum-cost allocation under a variance constraint, and multi-domain allocation with controlled precision. Handles lower and upper bounds, cost constraints, and multiple domains. Includes helper functions for variance computation, allocation summaries, rounding, and example datasets for testing and benchmarking.

This package provides indices and tools for directed acyclic graphs (DAGs), particularly DAG representations of intermittent streams. A detailed introduction to the package can be found in the publication: "Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG" (Aho et al., 2023) <doi:10.1016/j.envsoft.2023.105775>, and in the introductory package vignette.

This package provides tools to generate, visualize, and audit geometric string art figures for mathematics teaching. The package includes functions for circular, cardioid-like, elliptical, triangular, polygonal, star, parabolic, net-based, radial, hexagonal, lotus-like, rose-like, spiral, Lissajous, grid-based, decimal, and contour-based string art patterns. Each function returns peg coordinates, connection tables, total string length, audit information, and metadata, supporting educational applications in geometry, analytic geometry, modular arithmetic, trigonometry, rational numbers, and visual mathematics.

For making Trellis-type conditioning plots without strip labels. This is useful for displaying the structure of results from factorial designs and other studies when many conditioning variables would clutter the display with layers of redundant strip labels. Settings of the variables are encoded by layout and spacing in the trellis array and decoded by a separate legend. The functionality is implemented by a single S3 generic strucplot() function that is a wrapper for the Lattice package's xyplot() function. This allows access to all Lattice graphics capabilities in the usual way.

The stratification of univariate populations under stratified sampling designs is implemented according to Khan et al. (2002) <doi:10.1177/0008068320020518> and Khan et al. (2015) <doi:10.1080/02664763.2015.1018674> in this library. It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions.

This package provides string parsing functionalities for generating plotnames, filenames and paths.

This package provides an extendable, performant and multithreaded alt-string implementation backed by C++ vectors and strings.

R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press.

Message translation is often managed with po files and the gettext programme, but sometimes another solution is needed. In contrast to po files, a more flexible approach is used as in the Fluent <https://projectfluent.org/> project with R Markdown snippets. The key-value approach allows easier handling of the translated messages.

This package provides a dynamic model of the big-picture, whole ecosystem effects of hydrodynamics, temperature, nutrients, and fishing on continental shelf marine food webs. The package is described in: Heath, M.R., Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) <doi:10.1111/2041-210X.13510> StrathE2E2: An R package for modelling the dynamics of marine food webs and fisheries. 8pp.

This package implements an approximate string matching version of R's native match function. It can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal string alignment), qgrams (q- gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences.