Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) <doi:10.1002/bimj.202200334>.

Regression context for the Partial Least Squares framework for Extreme values. Estimations of the Shrinkage for Extreme Partial Least-Squares (SEPaLS) estimators, an adaptation of the original Partial Least Squares (PLS) method tailored to the extreme-value framework. The SEPaLS project is a joint work by Stephane Girard, Hadrien Lorenzo and Julyan Arbel. R code to replicate the results of the paper is available at <https://github.com/hlorenzo/SEPaLS_simus>. Extremes within PLS was already studied by one of the authors, see M Bousebeta, G Enjolras, S Girard (2023) <doi:10.1016/j.jmva.2022.105101>.

Unsupervised text tokenizer focused on computational efficiency. Wraps the YouTokenToMe library <https://github.com/VKCOM/YouTokenToMe> which is an implementation of fast Byte Pair Encoding (BPE) <https://aclanthology.org/P16-1162/>.

This package provides a comprehensive resource for data on Taylor Swift songs. Data is included for all officially released studio albums, extended plays (EPs), and individual singles are included. Data comes from Genius (lyrics) and Spotify (song characteristics). Additional functions are included for easily creating data visualizations with color palettes inspired by Taylor Swift's album covers.

This package provides a framework to download, parse, and store text datasets on the disk and load them when needed. Includes various sentiment lexicons and labeled text data sets for classification and analysis.

Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.

This package provides a shared tsibble data easily communicates between htmlwidgets on both client and server sides, powered by crosstalk'. A shiny module is provided to visually explore periodic/aperiodic temporal patterns.

Compute a non-overlapping layout of text boxes to label multiple overlain curves. For each curve, iteratively search for an adjacent x,y position for the text box that does not overlap with the other curves. If this process fails, then offsets are computed to add to the y values for each curve, that results in sufficient space to add all of the text labels.

Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.

This package provides functions for estimation of wood volumes, number of logs, diameters along the stem and heights at which certain diameters occur, based on taper functions and other parameters. References: McTague, J. P., & Weiskittel, A. (2021). <doi:10.1139/cjfr-2020-0326>.

Total variation denoising can be used to approximate a given sequence of noisy observations by a piecewise constant sequence, with adaptively-chosen break points. An efficient linear-time algorithm for total variation denoising is provided here, based on Johnson (2013) <doi:10.1080/10618600.2012.681238>.

This package provides methods for handling the missing values outliers are introduced in this package. The recognized missing values and outliers are replaced using a model-based approach. The model may consist of both autoregressive components and external regressors. The methods work robust and efficient, and they are fully tunable. The primary motivation for writing the package was preprocessing of the energy systems data, e.g. power plant production time series, but the package could be used with any time series data. For details, see Narajewski et al. (2021) <doi:10.1016/j.softx.2021.100809>.

This package provides multiple water chemistry-based models and published empirical models in one standard format. As many models have been included as possible, however, users should be aware that models have varying degrees of accuracy and applicability. To learn more, read the references provided below for the models implemented. Functions can be chained together to model a complete treatment process and are designed to work in a tidyverse workflow. Models are primarily based on these sources: Benjamin, M. M. (2002, ISBN:147862308X), Crittenden, J. C., Trussell, R., Hand, D., Howe, J. K., & Tchobanoglous, G., Borchardt, J. H. (2012, ISBN:9781118131473), USEPA. (2001) <https://www.epa.gov/sites/default/files/2017-03/documents/wtp_model_v._2.0_manual_508.pdf>.

Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].

The goal of tosr is to create the Tree of Science from Web of Science (WoS) and Scopus data. It can read files from both sources at the same time. More information can be found in Valencia-Hernández (2020) <https://revistas.unal.edu.co/index.php/ingeinv/article/view/77718>.

This package provides rolling statistical functions based on date and time windows instead of n-lagged observations.

This package provides an interactive interface to the tfrmt package. Users can import, modify, and export tables and templates with little to no code.

Perform two types of analysis: 1) checking the goodness-of-fit of tree models to your single-cell gene expression data; and 2) deciding which tree best fits your data.

This package provides a common way of validating a biological assay for is through a procedure, where m levels of an analyte are measured with n replicates at each level, and if all m estimates of the coefficient of variation (CV) are less than some prespecified level, then the assay is declared validated for precision within the range of the m analyte levels. Two limitations of this procedure are: there is no clear statistical statement of precision upon passing, and it is unclear how to modify the procedure for assays with constant standard deviation. We provide tools to convert such a procedure into a set of m hypothesis tests. This reframing motivates the m:n:q procedure, which upon completion delivers a 100q% upper confidence limit on the CV. Additionally, for a post-validation assay output of y, the method gives an ``effective standard deviation interval of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where mu is the expected value of the assay output for that sample. Further, the m:n:q procedure can be straightforwardly applied to constant standard deviation assays. We illustrate these tools by applying them to a growth inhibition assay. This is an implementation of the methods described in Fay, Sachs, and Miura (2018) <doi:10.1002/sim.7528>.

ARIMA-model-based decomposition of quarterly and monthly time series data. The methodology is developed and described, among others, in Burman (1980) <DOI:10.2307/2982132> and Hillmer and Tiao (1982) <DOI:10.2307/2287770>.

This package provides tools for simulating and modeling traffic flow on road networks using spatial conditional autoregressive (CAR) models. The package represents road systems as graphs derived from OpenStreetMap data <https://www.openstreetmap.org/> and supports network-based spatial dependence, basic preprocessing, and visualization for spatial traffic analysis.

The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) <DOI:10.1016/j.ijforecast.2010.04.009>.

Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.

Provide functions to estimate the coefficients in high-dimensional linear regressions via a tuning-free and robust approach. The method was published in Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "A Tuning-free Robust and Efficient Approach to High-dimensional Regression", Journal of the American Statistical Association, 115:532, 1700-1714(JASAâ s discussion paper), <doi:10.1080/01621459.2020.1840989>. See also Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "Rejoinder to â A tuning-free robust and efficient approach to high-dimensional regression". Journal of the American Statistical Association, 115, 1726-1729, <doi:10.1080/01621459.2020.1843865>; Peng, B. and Wang, L. (2015), "An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression", Journal of Computational and Graphical Statistics, 24:3, 676-694, <doi:10.1080/10618600.2014.913516>; Clémençon, S., Colin, I., and Bellet, A. (2016), "Scaling-up empirical risk minimization: optimization of incomplete u-statistics", The Journal of Machine Learning Research, 17(1):2682â 2717; Fan, J. and Li, R. (2001), "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties", Journal of the American Statistical Association, 96:456, 1348-1360, <doi:10.1198/016214501753382273>.