This package implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way.

Description: Implementation of topological data analysis methods based on graph-theoretic approaches for discovering topological structures in data. The core algorithm constructs topological spaces from graphs following Nada et al. (2018) <doi:10.1002/mma.4726> "New types of topological structures via graphs".

This tool is extended from methods in Bio.SeqUtils.MeltingTemp of python. The melting temperature of nucleic acid sequences can be calculated in three method, the Wallace rule (Thein & Wallace (1986) <doi:10.1016/S0140-6736(86)90739-7>), empirical formulas based on G and C content (Marmur J. (1962) <doi:10.1016/S0022-2836(62)80066-7>, Schildkraut C. (2010) <doi:10.1002/bip.360030207>, Wetmur J G (1991) <doi:10.3109/10409239109114069>, Untergasser,A. (2012) <doi:10.1093/nar/gks596>, von Ahsen N (2001) <doi:10.1093/clinchem/47.11.1956>) and nearest neighbor thermodynamics (Breslauer K J (1986) <doi:10.1073/pnas.83.11.3746>, Sugimoto N (1996) <doi:10.1093/nar/24.22.4501>, Allawi H (1998) <doi:10.1093/nar/26.11.2694>, SantaLucia J (2004) <doi:10.1146/annurev.biophys.32.110601.141800>, Freier S (1986) <doi:10.1073/pnas.83.24.9373>, Xia T (1998) <doi:10.1021/bi9809425>, Chen JL (2012) <doi:10.1021/bi3002709>, Bommarito S (2000) <doi:10.1093/nar/28.9.1929>, Turner D H (2010) <doi:10.1093/nar/gkp892>, Sugimoto N (1995) <doi:10.1016/S0048-9697(98)00088-6>, Allawi H T (1997) <doi:10.1021/bi962590c>, Santalucia N (2005) <doi:10.1093/nar/gki918>), and it can also be corrected with salt ions and chemical compound (SantaLucia J (1996) <doi:10.1021/bi951907q>, SantaLucia J(1998) <doi:10.1073/pnas.95.4.1460>, Owczarzy R (2004) <doi:10.1021/bi034621r>, Owczarzy R (2008) <doi:10.1021/bi702363u>).

This package provides a tufte'-alike style for rmarkdown'. A modern take on the Tufte design for pdf and html vignettes, building on the tufte package with additional contributions from the knitr and ggtufte package, and also acknowledging the key influence of envisioned css'.

This package provides drop-in replacements for common R functions (mean(), sum(), sd(), min(), etc.) that default to na.rm = TRUE and issue warnings when missing values are removed. It handles some special cases. The table() default is set to useNA = ifany'.

Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) <doi:10.18637/jss.v098.i15>.

Collection of shiny widgets to support teal applications. Enables the manipulation of application layout and plot or table settings.

This package provides a suite of functions for analysing, interpreting, and visualising time-series features calculated from different feature sets from the theft package. Implements statistical learning methodologies described in Henderson, T., Bryant, A., and Fulcher, B. (2023) <doi:10.48550/arXiv.2303.17809>.

Interface to TensorFlow IO', Datasets and filesystem extensions maintained by `TensorFlow SIG-IO` <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.

This package implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) <doi:10.48550/arXiv.2506.19988>. Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses.

An implementation of a boosted Tweedie compound Poisson model proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/07350015.2016.1200981>. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing high-order interactions among predictors. This package is based on the gbm package originally developed by Greg Ridgeway.

This package provides a connector to the What3Words (http://what3words.com/) service, which represents each 3m by 3m square on earth with a unique trio of English-language words.

Defines a graphics device and functions for graphical output in terminal emulators that support graphical output. Currently terminals that support the Terminal Graphics Protocol (<https://sw.kovidgoyal.net/kitty/graphics-protocol/>) and terminal supporting Sixel (<https://en.wikipedia.org/wiki/Sixel>) are supported.

This package creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.

This package provides a comprehensive and user-friendly interface for accessing, manipulating, and analyzing country-level data from around the world. It allows users to retrieve detailed information on countries, including names, regions, continents, populations, currencies, calling codes, and more, all in a tidy data format. The package is designed to work seamlessly within the tidyverse ecosystem, making it easy to filter, arrange, and visualize country-level data in R.

An implementation of tidy speaker vowel normalization. This includes generic functions for defining new normalization methods for points, formant tracks, and Discrete Cosine Transform coefficients, as well as convenience functions implementing established normalization methods. References for the implemented methods are: Johnson, Keith (2020) <doi:10.5334/labphon.196> Lobanov, Boris (1971) <doi:10.1121/1.1912396> Nearey, Terrance M. (1978) <https://sites.ualberta.ca/~tnearey/Nearey1978_compressed.pdf> Syrdal, Ann K., and Gopal, H. S. (1986) <doi:10.1121/1.393381> Watt, Dominic, and Fabricius, Anne (2002) <https://www.latl.leeds.ac.uk/article/evaluation-of-a-technique-for-improving-the-mapping-of-multiple-speakers-vowel-spaces-in-the-f1-f2-plane/>.

Introduction of qenv S4 class, that facilitates code execution and reproducibility in teal applications.

This package provides classes for storing and manipulating taxonomic data. Most of the classes can be treated like base R vectors (e.g. can be used in tables as columns and can be named). Vectorized classes can store taxon names and authorities, taxon IDs from databases, taxon ranks, and other types of information. More complex classes are provided to store taxonomic trees and user-defined data associated with them.

Unicodes are not friendly to work with, and not all Unicodes are Emoji per se, making obtaining Emoji statistics a difficult task. This tool can help your experience of working with Emoji as smooth as possible, as it has the tidyverse style.

To make the semiparametric transformation models easier to apply in real studies, we introduce this R package, in which the MLE in transformation models via an EM algorithm proposed by Zeng D, Lin DY(2007) <doi:10.1111/j.1369-7412.2007.00606.x> and adaptive lasso method in transformation models proposed by Liu XX, Zeng D(2013) <doi:10.1093/biomet/ast029> are implemented. C++ functions are used to compute complex loops. The coefficient vector and cumulative baseline hazard function can be estimated, along with the corresponding standard errors and P values.

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including dplyr', stats', xts', forecast', slider', padr', recipes', and rsample'.

The data that is generated from independent and consecutive GillespieSSA runs for a generic biochemical network is formatted as rows and constitutes an observation. The first column of each row is the computed timestep for each run. Subsequent columns are used for the number of molecules of each participating molecular species or "metabolite" of a generic biochemical network. In this way TemporalGSSA', is a wrapper for the R-package GillespieSSA'. The number of observations must be at least 30. This will generate data that is statistically significant. TemporalGSSA', transforms this raw data into a simulation time-dependent and metabolite-specific trial. Each such trial is defined as a set of linear models (n >= 30) between a timestep and number of molecules for a metabolite. Each linear model is characterized by coefficients such as the slope, arbitrary constant, etc. The user must enter an integer from 1-4. These specify the statistical modality utilized to compute a representative timestep (mean, median, random, all). These arguments are mandatory and will be checked. Whilst, the numeric indicator "0" indicates suitability, "1" prompts the user to revise and re-enter their data. An optional logical argument controls the output to the console with the default being "TRUE" (curtailed) whilst "FALSE" (verbose). The coefficients of each linear model are averaged (mean slope, mean constant) and are incorporated into a metabolite-specific linear regression model as the dependent variable. The independent variable is the representative timestep chosen previously. The generated data is the imputed molecule number for an in silico experiment with (n >=30) observations. These steps can be replicated with multiple set of observations. The generated "technical replicates" can be statistically evaluated (mean, standard deviation) and will constitute simulation time-dependent molecules for each metabolite. For SSA-generated datasets with varying simulation times TemporalGSSA will generate a simulation time-dependent trajectory for each metabolite of the biochemical network under study. The relevant publication with the mathematical derivation of the algorithm is (2022, Journal of Bioinformatics and Computational Biology) <doi:10.1142/S0219720022500184>. The algorithm has been deployed in the following publications (2021, Heliyon) <doi:10.1016/j.heliyon.2021.e07466> and (2016, Journal of Theoretical Biology) <doi:10.1016/j.jtbi.2016.07.002>.

This package provides functions to scale, log-transform and fit linear models within a tidyverse'-style R code framework. Intended to smooth over inconsistencies in output of base R statistical functions, allowing ease of teaching, learning and daily use. Inspired by the tidy principles used in broom Robinson (2017) <doi:10.21105/joss.00341>.

This package contains functions to standardize tracheid profiles using the traditional method (Vaganov) and a new method to standardize tracheidograms based on the relative position of tracheids within tree rings.