Enables the analysis of spectroscopy data such as infrared ('IR'), Raman, and nuclear magnetic resonance ('NMR') using the tidy data framework from the tidyverse'. The tidyspec package provides functions for data transformation, normalization, baseline correction, smoothing, derivatives, and both interactive and static visualization. It promotes structured, reproducible workflows for spectral data exploration and preprocessing. Implemented methods include Savitzky and Golay (1964) "Smoothing and Differentiation of Data by Simplified Least Squares Procedures" <doi:10.1021/ac60214a047>, Sternberg (1983) "Biomedical Image Processing" <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1654163>, Zimmermann and Kohler (1996) "Baseline correction using the rolling ball algorithm" <doi:10.1016/0168-583X(95)00908-6>, Beattie and Esmonde-White (2021) "Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra" <doi:10.1177/0003702820987847>, Wickham et al. (2019) "Welcome to the tidyverse" <doi:10.21105/joss.01686>, and Kuhn, Wickham and Hvitfeldt (2024) "recipes: Preprocessing and Feature Engineering Steps for Modeling" <https://CRAN.R-project.org/package=recipes>.

Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.

Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).

This package provides methods for generating modelled parametric Tropical Cyclone (TC) spatial hazard fields and time series output at point locations from TC tracks. R's compatibility to simply use fast cpp code via the Rcpp package and the wide range spatial analysis tools via the terra package makes it an attractive open source environment to study TCs'. This package estimates TC vortex wind and pressure fields using parametric equations originally coded up in python by TCRM <https://github.com/GeoscienceAustralia/tcrm> and then coded up in Cuda cpp by TCwindgen <https://github.com/CyprienBosserelle/TCwindgen>.

This package provides methods for generating .dat files for use with the AMPL software using spatial data, particularly rasters. It includes support for various spatial data formats and different problem types. By automating the process of generating AMPL datasets, this package can help streamline optimization workflows and make it easier to solve complex optimization problems. The methods implemented in this package are described in detail in a publication by Fourer et al. (<doi:10.1287/mnsc.36.5.519>).

This package provides functions to get personal Google Scholar profile data from web API and show it in table or figure format.

This package provides a lightweight and focused text annotation tool built with shiny'. Provides an interactive graphical user interface for coding text documents, managing code hierarchies, creating memos, and analyzing coding patterns. Features include code co-occurrence analysis, visualization of coding patterns, comparison of multiple coding sets, and export capabilities. Supports collaborative qualitative research through standardized annotation formats and analysis tools.

An extension to the R tidy data environment for automated machine learning. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data.

Computation and visualization of Taxicab Correspondence Analysis, Choulakian (2006) <doi:10.1007/s11336-004-1231-4>. Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod). Classical CA is based on the Euclidean distance. Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance. For some tables, Taxicab CA gives more informative results than classical CA.

Access Google Trends information. This package provides a tidy wrapper to the gtrendsR package. Use four spaces when indenting paragraphs within the Description.

This package performs the detection of linear trend changes for univariate time series by implementing the bottom-up unbalanced wavelet transformation proposed by H. Maeng and P. Fryzlewicz (2023). The estimated number and locations of the change-points are returned with the piecewise-linear estimator for signal.

Features include the ability to extract tabled content from NISO-JATS-coded XML, any native HTML or HML file, DOCX, and PDF documents, and then collapse it into a text format that is readable by humans by mimicking the actions of a screen reader. As tables within PDF documents are extracted with the tabulapdf package, and the table captions and footnotes cannot be extracted, the results on tables within PDF documents have to be considered less precise. The function table2matrix() returns a list of the tables within a document as character matrices. table2text() collapses the matrix content into a list of character strings by imitating the behavior of a screen reader. The textual representation of characters and numbers can be unified with unifyMatrix() before parsing. The function table2stats() extracts the tabled statistical test results from the collapsed text with the function standardStats() from the JATSdecoder package and, if activated, checks the reported and coded p-values for consistency. Due to the great variability and potential complexity of table structures, parsing accuracy may vary.

Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average (TARMA) models. It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) <doi:10.1080/07350015.2024.2412011>; tests for threshold effects, see Giannerini et al. JoE (2024) <doi:10.1016/j.jeconom.2023.01.004>, Goracci et al. Statistica Sinica (2023) <doi:10.5705/ss.202021.0120>, Angelini et al. (2024) <doi:10.48550/arXiv.2308.00444>; unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) <doi:10.5705/ss.202022.0125>.

This package provides a set of tools designed to perform descriptive data analysis on assets, manage asset portfolios and capital allocation, and download, organize, and maintain data from the "Tehran Stock Exchange" and "NOBITEX" platforms.

Instead of nesting function calls, annotate and transform functions using "#." comments.

The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. TCGAretriever helps accessing and downloading TCGA data hosted on cBioPortal via its Web Interface (see <https://www.cbioportal.org/> for more information).

It offers functions for splitting, parsing, tokenizing and creating a vocabulary for big text data files. Moreover, it includes functions for building a document-term matrix and extracting information from those (term-associations, most frequent terms). It also embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. Lastly, it includes functions for Word Vector Representations (i.e. GloVe', fasttext') and incorporates functions for the calculation of (pairwise) text document dissimilarities. The source code is based on C++11 and exported in R through the Rcpp', RcppArmadillo and BH packages.

This package provides functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability.

Univariate time series operations that follow an opinionated design. The main principle of transx is to keep the number of observations the same. Operations that reduce this number have to fill the observations gap.

Fit Thurstonian Item Response Theory (IRT) models in R. This package supports fitting Thurstonian IRT models and its extensions using Stan', lavaan', or Mplus for the model estimation. Functionality for extracting results, making predictions, and simulating data is provided as well. References: Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>; Bürkner et al. (2019) <doi:10.1177/0013164419832063>.

This package provides tools to deploy TensorFlow <https://www.tensorflow.org/> models across multiple services. Currently, it provides a local server for testing cloudml compatible services.

The ts objects in R are managed using a very specific date format (in the form c(2022, 9) for September 2022 or c(2021, 2) for the second quarter of 2021, depending on the frequency, for example). We focus solely on monthly and quarterly series to manage the dates of ts objects. The general idea is to offer a set of functions to manage this date format without it being too restrictive or too imprecise depending on the rounding. This is a compromise between simplicity, precision and use of the basic stats functions for creating and managing time series (ts(), window()). Les objets ts en R sont gérés par un format de date très particulier (sous la forme c(2022, 9) pour septembre 2022 ou c(2021, 2) pour le deuxième trimestre 2021 selon la fréquence par exemple). On se concentre uniquement sur les séries mensuelles et trimestrielles pour gérer les dates des objets ts. Lidée générale est de proposer un ensemble de fonctions pour gérer ce format de date sans que ce soit trop contraignant ou trop imprécis selon les arrondis. Cest un compromis entre simplicité, précision et utilisation des fonctions du package stats de création et de gestion des séries temporelles (ts(), window()).

Find topics in texts which are semantically embedded using techniques like word2vec or Glove. This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic. The techniques are explained in detail in the paper Topic Modeling in Embedding Spaces by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at <doi:10.48550/arXiv.1907.04907>.

The main goal of the R package treeDbalance is to provide functions for the computation of several measurements of 3D node imbalance and their respective 3D tree imbalance indices, as well as to introduce the new phylo3D format for rooted 3D tree objects. Moreover, it encompasses an example dataset of 3D models of 63 beans in phylo3D format. Please note that this R package was developed alongside the project described in the manuscript Measuring 3D tree imbalance of plant models using graph-theoretical approaches by M. Fischer, S. Kersting, and L. Kühn (2023) <arXiv:2307.14537>, which provides precise mathematical definitions of the measurements. Furthermore, the package contains several helpful functions, for example, some auxiliary functions for computing the ancestors, descendants, and depths of the nodes, which ensures that the computations can be done in linear time. Most functions of treeDbalance require as input a rooted tree in the phylo3D format, an extended phylo format (as introduced in the R package ape 1.9 in November 2006). Such a phylo3D object must have at least two new attributes next to those required by the phylo format: node.coord', the coordinates of the nodes, as well as edge.weight', the literal weight or volume of the edges. Optional attributes are edge.diam', the diameter of the edges, and edge.length', the length of the edges. For visualization purposes one can also specify edge.type', which ranges from normal cylinder to bud to leaf, as well as edge.color to change the color of the edge depiction. This project was supported by the joint research project DIG-IT! funded by the European Social Fund (ESF), reference: ESF/14-BM-A55-0017/19, and the Ministry of Education, Science and Culture of Mecklenburg-Western Pomerania, Germany, as well as by the the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM â Artificial Intelligence in Farming funded by the German Federal Ministry of Education and Research (FKZ: 03WIR4805).