Improves the predictive performance of ridge and lasso regression exploiting one or more sources of prior information on the importance and direction of effects (Rauschenberger and others 2023, <doi:10.1093/bioinformatics/btad680>). For running the vignette (optional), install fwelnet and ecpc from <https://github.com/kjytay/fwelnet> and <https://github.com/Mirrelijn/ecpc>, respectively.

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.

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>.

Inferring causation from time series data through empirical dynamic modeling (EDM), with methods such as convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping as outlined in Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality as described in Tao et al. (2023) <doi:10.1016/j.fmre.2023.01.007>.

The ta-test is a modified two-sample or two-group t-test of Gosset (1908). In small samples with less than 15 replicates,the ta-test significantly reduces type I error rate but has almost the same power with the t-test and hence can greatly enhance reliability or reproducibility of discoveries in biology and medicine. The ta-test can test single null hypothesis or multiple null hypotheses without needing to correct p-values.

Generate tables, listings, and graphs (TLG) using tidyverse'. Tables can be created functionally, using a standard TLG process, or by specifying table and column metadata to create generic analysis summaries. The envsetup package can also be leveraged to create environments for table creation.

Estimators for two functionals used to detect Gamma, Pareto or Lognormal distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) <doi:10.1111/stan.12316> and Klar (2024) <doi:10.1080/00031305.2024.2413081>. One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) <doi:10.3390/risks5010010>, distinguishes between log-convex and log-concave tail behavior. Furthermore the characterization of the lognormal distribution is based on the work of Mosimann (1970) <doi:10.2307/2284599>. The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values.

This package provides methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.

The main function of the package aims to update lmer()'/'glmer() models depending on their warnings, so trying to avoid convergence and singularity problems.

This package provides tidyverse methods for wrangling and analyzing Earth Engine <https://earthengine.google.com/> data. These methods help the user with filtering, joining and summarising Earth Engine image collections.

This package provides a tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.

This package provides ggplot2 geoms for drawing treemaps.

Implement the alternating algorithm for supervised tensor decomposition with interactive side information. Details can be found in the publication Hu, Jiaxin, Chanwoo Lee, and Miaoyan Wang. "Generalized Tensor Decomposition with features on multiple modes." Journal of Computational and Graphical Statistics, Vol. 31, No. 1, 204-218, 2022 <doi:10.1080/10618600.2021.1978471>.

Unleash the power of time-series data visualization with ease using our package. Designed with simplicity in mind, it offers three key features through the shiny package output. The first tab shows time- series charts with forecasts, allowing users to visualize trends and changes effortlessly. The second one displays Averages per country presented in tables with accompanying sparklines, providing a quick and attractive overview of the data. The last tab presents A customizable world map colored based on user-defined variables for any chosen number of countries, offering an advanced visual approach to understanding geographical data distributions. This package operates with just a few simple arguments, enabling users to conduct sophisticated analyses without the need for complex programming skills. Transform your time-series data analysis experience with our user-friendly tool.

This package implements measures of tree similarity, including information-based generalized Robinson-Foulds distances (Phylogenetic Information Distance, Clustering Information Distance, Matching Split Information Distance; Smith 2020) <doi:10.1093/bioinformatics/btaa614>; Jaccard-Robinson-Foulds distances (Bocker et al. 2013) <doi:10.1007/978-3-642-40453-5_13>, including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>; the Matching Split Distance (Bogdanowicz & Giaro 2012) <doi:10.1109/TCBB.2011.48>; the Hierarchical Mutual Information (Perotti et al. 2015) <doi:10.1103/PhysRevE.92.062825>; Maximum Agreement Subtree distances; the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. (1996) <doi:10.1007/3-540-61332-3_168>. Includes tools for visualizing mappings of tree space (Smith 2022) <doi:10.1093/sysbio/syab100>, for identifying islands of trees (Silva and Wilkinson 2021) <doi:10.1093/sysbio/syab015>, for calculating the median of sets of trees, and for computing the information content of trees and splits.

This package provides functions to calculate the Surface Temperature (Ts) from geospatial raster data. These functions use albedo, Normalized Difference Vegetation Index (NDVI), and air temperature (Ta) to estimate Ts, facilitating hydrological, ecological, and remote sensing analyses.

Optimize and compress images using Rust libraries to reduce file sizes while maintaining image quality. Supports PNG palette reduction and dithering via the exoquant crate before lossless PNG optimization via the oxipng crate. The package provides functions to optimize individual image files or entire directories, with configurable compression levels.

Temperature measurement data, equations and methods for thermocouples, wire RTD, thermistors, IC thermometers, bimetallic strips and the ITS-90.

Statistical interpretation of forensic glass transfer (Simulation of the probability distribution of recovered glass fragments).

The ToxCast Data Analysis Pipeline ('tcpl') is an R package that manages, curve-fits, plots, and stores ToxCast data to populate its linked MySQL database, invitrodb'. The package was developed for the chemical screening data curated by the US EPA's Toxicity Forecaster (ToxCast) program, but tcpl can be used to support diverse chemical screening efforts.

Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by ggplot2 instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) <https://otexts.com/fpp3/>.

Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Create maps and perform spatial analysis on the routes and stops. Please see the GTFS documentation here for more detail: <https://gtfs.org/>.

This package provides a collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) <doi:10.1111/rssb.12162> and Botev and L'Ecuyer (2015) <doi:10.1109/WSC.2015.7408180>.

This package provides a pipeline of tools for analysing circadian time-series data using functional data analysis (FDA). The package supports smoothing of rhythmic time series, functional principle component analysis (FPCA), and extraction of group-level traits from functional representations. Analyses can incorporate multiple curve derivatives and optional temporal segmentation, enabling comparative analysis of circadian dynamics across experimental groups and time windows.