Stringr is a consistent, simple and easy to use set of wrappers around the fantastic stringi package. All function and argument names (and positions) are consistent, all functions deal with "NA"'s and zero length vectors in the same way, and the output from one function is easy to feed into the input of another.

An implementation of Simultaneous Truth and Performance Level Estimation (STAPLE) <doi:10.1109/TMI.2004.828354>. This method is used when there are multiple raters for an object, typically an image, and this method fuses these ratings into one rating. It uses an expectation-maximization method to estimate this rating and the individual specificity/sensitivity for each rater.

This package implements stacked elastic net regression (Rauschenberger 2021 <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005 <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).

This package provides a collection of algorithms and functions to aid statistical modeling. It includes growth curve comparisons, limiting dilution analysis (aka ELDA), mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models. It also includes advanced generalized linear model functions that implement secure convergence, dispersion modeling and Tweedie power-law families.

Collection of model estimation, and model plotting functions related to the STEPCAM family of community assembly models. STEPCAM is a STEPwise Community Assembly Model that infers the relative contribution of Dispersal Assembly, Habitat Filtering and Limiting Similarity from a dataset consisting of the combination of trait and abundance data. See also <doi:10.1890/14-0454.1> for more information.

This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) <doi:10.1121/1.1977523> on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) <doi:10.1039/c3ay41160e>. The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.'.

Decompose a time series into seasonal, trend, and remainder components using an implementation of Seasonal Decomposition of Time Series by Loess (STL) that provides several enhancements over the STL method in the stats package. These enhancements include handling missing values, providing higher order (quadratic) loess smoothing with automated parameter choices, frequency component smoothing beyond the seasonal and trend components, and some basic plot methods for diagnostics.

For a single, known pathogen phylogeny, provides functions for enumeration of the set of compatible epidemic transmission trees, and for uniform sampling from that set. Optional arguments allow for incomplete sampling with a known number of missing individuals, multiple sampling, and known infection time limits. Always assumed are a complete transmission bottleneck and no superinfection or reinfection. See Hall and Colijn (2019) <doi:10.1093/molbev/msz058> for methodology.

STADyUM is a package with functionality for analyzing nascent RNA read counts to infer transcription rates. This includes utilities for processing experimental nascent RNA read counts as well as for simulating PRO-seq data. Rates such as initiation, pause release and landing pad occupancy are estimated from either synthetic or experimental data. There are also options for varying pause sites and including steric hindrance of initiation in the model.

This package allows for fast, correct, consistent, portable, as well as convenient character string/text processing in every locale and any native encoding. Owing to the use of the ICU library, the package provides R users with platform-independent functions known to Java, Perl, Python, PHP, and Ruby programmers. Among available features there are: pattern searching (e.g. via regular expressions), random string generation, string collation, transliteration, concatenation, date-time formatting and parsing, etc.

Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.

This package contains more modern tools for causal inference using regression standardization. Four general classes of models are implemented; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models, and shared frailty gamma-Weibull models. Methodological details are described in Sjölander, A. (2016) <doi:10.1007/s10654-016-0157-3>. Also includes functionality for doubly robust estimation for generalized linear models in some special cases, and the ability to implement custom models.

Offers a suite of functions for converting to and from (atomic) vectors, matrices, data.frames, and (3D+) arrays as well as lists of these objects. It is an alternative to the base R as.<str>.<method>() functions (e.g., as.data.frame.array()) that provides more useful and/or flexible restructuring of R objects. To do so, it only works with common structuring of R objects (e.g., data.frames with only atomic vector columns).

This package provides functions to model and forecast crop yields using a spatial temporal conditional copula approach. The package incorporates extreme weather covariates and Bayesian Structural Time Series models to analyze crop yield dependencies across multiple regions. Includes tools for fitting, simulating, and visualizing results. This method build upon established R packages, including Hofert et al'. (2025) <doi:10.32614/CRAN.package.copula>, Scott (2024) <doi:10.32614/CRAN.package.bsts>, and Stephenson et al'. (2024) <doi:10.32614/CRAN.package.evd>.

This package provides functions for stabilometric signal quantification. The input is a data frame containing the x, y coordinates of the center-of-pressure displacement. Jose Magalhaes de Oliveira (2017) <doi:10.3758/s13428-016-0706-4> "Statokinesigram normalization method"; T E Prieto, J B Myklebust, R G Hoffmann, E G Lovett, B M Myklebust (1996) <doi:10.1109/10.532130> "Measures of postural steadiness: Differences between healthy young and elderly adults"; L F Oliveira et al (1996) <doi:10.1088/0967-3334/17/4/008> "Calculation of area of stabilometric signals using principal component analisys".

StabMap performs single cell mosaic data integration by first building a mosaic data topology, and for each reference dataset, traverses the topology to project and predict data onto a common embedding. Mosaic data should be provided in a list format, with all relevant features included in the data matrices within each list object. The output of stabMap is a joint low-dimensional embedding taking into account all available relevant features. Expression imputation can also be performed using the StabMap embedding and any of the original data matrices for given reference and query cell lists.

Statnet is a collection of packages for statistical network analysis that are designed to work together because they share common data representations and API design. They provide an integrated set of tools for the representation, visualization, analysis, and simulation of many different forms of network data. This package is designed to make it easy to install and load the key statnet packages in a single step. Learn more about statnet at <http://www.statnet.org>. Tutorials for many packages can be found at <https://github.com/statnet/Workshops/wiki>. For an introduction to functions in this package, type help(package='statnet').

Forms queries to submit to the Cleveland Federal Reserve Bank web site's financial stress index data site. Provides query functions for both the composite stress index and the components data. By default the download includes daily time series data starting September 25, 1991. The functions return a class of either type easing or cfsi which contain a list of items related to the query and its graphical presentation. The list includes the time series data as an xts object. The package provides four lattice time series plots to render the time series data in a manner similar to the bank's own presentation.

An introduction to a couple of novel predictive variable selection methods for generalised boosted regression modeling (gbm). They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the steprf package, and also based on predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.

Generates artificial point patterns marked by their spatial and temporal signatures. The resulting point cloud may exhibit inherent interactions between both signatures. The simulation integrates microsimulation (Holm, E., (2017)<doi:10.1002/9781118786352.wbieg0320>) and agent-based models (Bonabeau, E., (2002)<doi:10.1073/pnas.082080899>), beginning with the configuration of movement characteristics for the specified agents (referred to as walkers') and their interactions within the simulation environment. These interactions (Quaglietta, L. and Porto, M., (2019)<doi:10.1186/s40462-019-0154-8>) result in specific spatiotemporal patterns that can be visualized, analyzed, and used for various analytical purposes. Given the growing scarcity of detailed spatiotemporal data across many domains, this package provides an alternative data source for applications in social and life sciences.

This package produces publication-ready statistical tables and figures formatted according to the 7th edition of the American Psychological Association (APA) style guidelines. Supports descriptive statistics, t-tests, z-tests, chi-square tests, Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), two-way ANOVA with simple effects, Multivariate Analysis of Variance (MANOVA), robust and cluster-robust regression using Heteroscedasticity-Consistent (HC) standard errors, post-hoc pairwise comparisons, homoskedasticity and heteroscedasticity diagnostics including the Non-Constant Variance (NCV) test, proportion tests, and multilevel mixed-effects models with intraclass correlation coefficients (ICC) and model-comparison tables. Output can be directed to the console, Microsoft Word (via officer and flextable'), or LaTeX. For APA style guidelines see American Psychological Association (2020, ISBN:978-1-4338-3216-1).

Implementations of stochastic, limited-memory quasi-Newton optimizers, similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm, for smooth stochastic optimization. Implements the following methods: oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>), SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>), adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>). Provides functions for easily creating R objects with partial_fit/predict methods from some given objective/gradient/predict functions. Includes an example stochastic logistic regression using these optimizers. Provides header files and registered C routines for using it directly from C/C++.

Stepwise regression is a statistical technique used for model selection. This package streamlines stepwise regression analysis by supporting multiple regression types(linear, Cox, logistic, Poisson, Gamma, and negative binomial), incorporating popular selection strategies(forward, backward, bidirectional, and subset), and offering essential metrics. It enables users to apply multiple selection strategies and metrics in a single function call, visualize variable selection processes, and export results in various formats. StepReg offers a data-splitting option to address potential issues with invalid statistical inference and a randomized forward selection option to avoid overfitting. We validated StepReg's accuracy using public datasets within the SAS software environment. For an interactive web interface, users can install the companion StepRegShiny package. The methodology is described in Li et al. (2026) <doi:10.32614/RJ-2026-005>.

English is the native language for only 5% of the World population. Also, only 17% of us can understand this text. Moreover, the Latin alphabet is the main one for merely 36% of the total. The early computer era, now a very long time ago, was dominated by the US. Due to the proliferation of the internet, smartphones, social media, and other technologies and communication platforms, this is no longer the case. This package replaces base R string functions (such as grep(), tolower(), sprintf(), and strptime()) with ones that fully support the Unicode standards related to natural language and date-time processing. It also fixes some long-standing inconsistencies, and introduces some new, useful features. Thanks to ICU (International Components for Unicode) and stringi', they are fast, reliable, and portable across different platforms.