Offers a fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization. Reference: Congrui Yi and Jian Huang (2017) <doi:10.1080/10618600.2016.1256816>.

Generates region-specific Suess and Laws corrections for stable carbon isotope data from marine organisms collected between 1850 and 2023. Version 0.1.6 of SuessR contains four built-in regions: the Bering Sea ('Bering Sea'), the Aleutian archipelago ('Aleutian Islands'), the Gulf of Alaska ('Gulf of Alaska'), and the subpolar North Atlantic ('Subpolar North Atlantic'). Users can supply their own environmental data for regions currently not built into the package to generate corrections for those regions.

Calibration and discrimination metrics for spaced-repetition memory models. Provides the sample-weighted binned root mean squared error (RMSE(bins)) used to rank schedulers in the open spaced repetition benchmark, together with log loss, the area under the ROC curve, and calibration curves.

This is a wrapper of the React library React-Toastify'. It allows to show some notifications (toasts) in Shiny applications. There are options for the style, the position, the transition effect, and more.

Derives stratified prevalence tables from the condition, procedure, and drug records in OMOP CDM (Observational Medical Outcomes Partnership Common Data Model) databases, computes log2 prevalence ratios between paired datasets, and synthesizes them via random-effects meta-analysis at multiple aggregation levels (year, age group, and sex). Between-study variance is estimated with the Paule-Mandel method, as described in Paule and Mandel (1982) <doi:10.6028/jres.087.022>.

This package provides a facility to generate balanced semi-Latin rectangles with any cell size (preferably up to ten) with given number of treatments, see Uto, N.P. and Bailey, R.A. (2020). "Balanced Semi-Latin rectangles: properties, existence and constructions for block size two". Journal of Statistical Theory and Practice, 14(3), 1-11, <doi:10.1007/s42519-020-00118-3>. It also provides facility to generate partially balanced semi-Latin rectangles for cell size 2, 3 and 4 for any number of treatments.

This package provides a scrolling chat interface with multiline input, suitable for creating chatbot apps based on Large Language Models (LLMs). Designed to work particularly well with the ellmer R package for calling LLMs.

This package implements the basic elements of the multi-model inference paradigm for up to twenty species-area relationship models (SAR), using simple R list-objects and functions, as in Triantis et al. 2012 <DOI:10.1111/j.1365-2699.2011.02652.x>. The package is scalable and users can easily create their own model and data objects. Additional SAR related functions are provided.

Use inverse probability weighting methods to estimate treatment effect under marginal structure model (MSM) for the transition hazard of semi competing risk data, i.e. illness death model. We implement two specific such models, the usual Markov illness death structural model and the general Markov illness death structural model. We also provide the predicted three risks functions from the marginal structure models. Zhang, Y. and Xu, R. (2022) <arXiv:2204.10426>.

Calculates parameters of the seawater carbonate system and assists in design of ocean acidification perturbation experiments.

Fast Multiplication and Marginalization of Sparse Tables <doi:10.18637/jss.v111.i02>.

This package implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma progression using visual field data", <doi:10.1016/j.spasta.2019.02.001>.

Basic functions for dealing with wav files and sound samples.

This package performs hybrid multi-stage factor analytic procedure for controlling acquiescence in restricted solutions (Ferrando & Lorenzo-Seva, 2000 <https://www.uv.es/revispsi/articulos3.00/ferran7.pdf>).

Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.

Fits singular linear models to longitudinal data. Singular linear models are useful when the number, or timing, of longitudinal observations may be informative about the observations themselves. They are described in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general longitudinal data.

Remove batch effects by projecting query batches into the reference batch space.

This package provides a collection of Radix Tree and Trie algorithms for finding similar sequences and calculating sequence distances (Levenshtein and other distance metrics). This work was inspired by a trie implementation in Python: "Fast and Easy Levenshtein distance using a Trie." Hanov (2011) <https://stevehanov.ca/blog/index.php?id=114>. It also includes a modified version of the Starcode all-pairs search algorithm (Zorita, Cuscó, and Filion 2015) <doi:10.1093/bioinformatics/btv053>.

This package provides functionality to fit and simulate from stationary vine copula models for time series, see Nagler et al. (2022) <doi:10.1016/j.jeconom.2021.11.015>.

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.

It fits scale mixture of skew-normal linear mixed models using either an expectationâ maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence <doi:10.18637/jss.v115.i07>.

This package provides a collection of data processing, visualization, and export functions to support soil survey operations. Many of the functions build on the `SoilProfileCollection` S4 class provided by the aqp package, extending baseline visualization to more elaborate depictions in the context of spatial and taxonomic data. While this package is primarily developed by and for the USDA-NRCS, in support of the National Cooperative Soil Survey, the authors strive for generalization sufficient to support any soil survey operation. Many of the included functions are used by the SoilWeb suite of websites and movile applications. These functions are provided here, with additional documentation, to enable others to replicate high quality versions of these figures for their own purposes.

This package implements a custom matrix input field.

This package provides a simple function that anonymises a list of variables in a consistent way: anonymised factors are not recycled and the same original levels receive the same anonymised factor even if located in different datasets.