This package provides functions to build interactive dashboards combining the Tabler UI Kit with Shiny', making it easy to create professional-looking web applications. Tabler is fully responsive and compatible with all modern browsers. Offers customizable layouts and components built with HTML5 and CSS3'. The underlying Tabler (<https://github.com/tabler/tabler>) and Tabler Icons (<https://github.com/tabler/tabler-icons>) were pre-built from source to eliminate the need for Node.js and NPM on package installation.

The main objective of cooperative Transferable-Utility games (TU-games) is to allocate a good among the agents involved. The package implements major solution concepts including the Shapley value, Banzhaf value, and egalitarian rules, alongside their extensions for structured games: the Owen value and Banzhaf-Owen value for games with a priori unions, and the Myerson value for communication games on networks. To address the inherent exponential computational complexity of exact evaluation, the package offers both exact algorithms and linear approximation methods based on sampling, enabling the analysis of large-scale games. Additionally, it supports core set-based solutions, allowing computation of the vertices and the centroid of the core.

This package implements inverse and augmented inverse probability weighted estimators for common treatment effect parameters at an interim analysis with time-lagged outcome that may not be available for all enrolled subjects. Produces estimators, standard errors, and information that can be used to compute stopping boundaries using software that assumes that the estimators/test statistics have independent increments. Tsiatis, A. A. and Davidian, M., (2022) <doi:10.1002/sim.9580> .

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. Tracer (<https://github.com/beast-dev/tracer/>) is a GUI tool to parse and analyze the files generated by BEAST2'. This package provides a way to parse and analyze BEAST2 input files without active user input, but using R function calls instead.

This package provides model specifications, tuning parameters for models in dann package. Models based on Hastie (1996) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.

This package implements the multiway sparse clustering approach of M. Wang and Y. Zeng, "Multiway clustering via tensor block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019.

Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.

GUI for entering test items and obtaining raw and transformed scores. The results are shown on the console and can be saved to a tabular text file for further statistical analysis. The user can define his own tests and scoring procedures through a GUI.

Test the nullity of covariances, in a set of variables, using a simple univariate procedure. See Marques, Diago, Norouzirad, Bispo (2023) <doi:10.1002/mma.9130>.

Write output (plots and tables) ensuring traceability back to code. Includes a graphics saver with simple automation of stamping with source, destination and creation time. A list of plots can be saved at once. A user-friendly selection of output dimensions for presentations, on-screen inspections, and more available.

Likelihood ratio and maximum likelihood statistics are provided that can be used as alternatives to p-values Colquhoun (2017) <doi:10.1098/rsos.171085>. Arguments can be either p-values or t-statistics. together with degrees of freedom. For the function tTOlr', the argument twoSided has the default twoSided = TRUE'.

This package implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. Functions are written with torch tensors and provide CRPS (Continuous Ranked Probability Scores) training plus mixture-distribution post-processing.

Builds tables with customizable rows. Users can specify the type of data to use for each row, as well as how to handle missing data and the types of comparison tests to run on the table columns.

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>).

Record all tree-ring Shapefile of tree disk with GIS soft Qgis and interpolating model from high resolution tree disk image.

Streamline the processing of Telraam data, sourced from open data mobility sensors. These tools range from data retrieval (without the need for API knowledge) to data visualization, including data preprocessing.

Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of heterogeneous sources into a three-dimensional matrix of signals (features by observations by sources). The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) <DOI:10.1038/s41467-019-11052-9>.

This package provides a collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes. These tools are derived from Anderson et al. (2001) <doi:10.1016/S0047-2727(00)00085-2> and Froeb et al. (2003) <doi:10.1016/S0304-4076(02)00166-5>.

This is a statistical tool interactive that provides multivariate statistical tests that are more powerful than traditional Hotelling T2 test and LRT (likelihood ratio test) for the vector of normal mean populations with and without contamination and non-normal populations (Henrique J. P. Alves & Daniel F. Ferreira (2019) <DOI: 10.1080/03610918.2019.1693596>).

This package provides a two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.

This package implements geodesic interpolation and basis generation functions that allow you to create new tour methods from R.

This package implements methods and functions to calibrate time-specific niche models (multi-temporal calibration), letting users execute a strict calibration and selection process of niche models based on ellipsoids, as well as functions to project the potential distribution in the present and in global change scenarios.The tenm package has functions to recover information that may be lost or overlooked while applying a data curation protocol. This curation involves preserving occurrences that may appear spatially redundant (occurring in the same pixel) but originate from different time periods. A novel aspect of this package is that it might reconstruct the fundamental niche more accurately than mono-calibrated approaches. The theoretical background of the package can be found in Peterson et al. (2011)<doi:10.5860/CHOICE.49-6266>.

This package performs sparse discriminant analysis on a combination of node and leaf predictors when the predictor variables are structured according to a tree, as described in Fukuyama et al. (2017) <doi:10.1371/journal.pcbi.1005706>.

This package provides a universal non-uniform random number generator for quite arbitrary distributions with piecewise twice differentiable densities.