This package provides a system for personalized exercise plan recommendations for T2D (Type 2 Diabetes) patients based on the primary outcome of HbA1c (Glycated Hemoglobin). You provide the individual's information, and T2DFitTailor details the exercise plan and predicts the intervention's effectiveness.

TidyTuesday is a project by the Data Science Learning Community in which they post a weekly dataset in a public data repository (<https://github.com/rfordatascience/tidytuesday>) for people to analyze and visualize. This package provides the tools to easily download this data and the description of the source.

In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, <DOI:10.1080/10618600.2019.1592758>. The fitted functions are piecewise polynomial with adaptively chosen knots.

This package creates geographic map tiles from geospatial map files or non-geographic map tiles from simple image files. This package provides a tile generator function for creating map tile sets for use with packages such as leaflet'. In addition to generating map tiles based on a common raster layer source, it also handles the non-geographic edge case, producing map tiles from arbitrary images. These map tiles, which have a non-geographic, simple coordinate reference system (CRS), can also be used with leaflet when applying the simple CRS option. Map tiles can be created from an input file with any of the following extensions: tif, grd and nc for spatial maps and png, jpg and bmp for basic images. This package requires Python and the gdal library for Python'. Windows users are recommended to install OSGeo4W (<https://trac.osgeo.org/osgeo4w/>) as an easy way to obtain the required gdal support for Python'.

This package provides functions for tabulating and summarising categorical variables. Most functions are designed to work with dataframes, and use the tidyverse idiom of taking the dataframe as the first argument so they work within pipelines. Equivalent functions that operate directly on vectors are also provided where it makes sense. This package aims to make exploratory data analysis involving categorical variables quicker, simpler and more robust.

Simple tabulation should be dead simple. This package is an opinionated approach to easy tabulations while also providing exact numbers and allowing for re-usability. This is achieved by providing tabulations as data.frames with columns for values, optional variable names, frequency counts including and excluding NAs and percentages for counts including and excluding NAs. Also values are automatically sorted by in decreasing order of frequency counts to allow for fast skimming of the most important information.

Access open data from <https://www.threesixtygiving.org>, a database of charitable grant giving in the UK operated by 360Giving'. The package provides functions to search and retrieve data on charitable grant giving, and process that data into tidy formats. It relies on the 360Giving data standard, described at <https://standard.threesixtygiving.org/>.

This package provides a terribly-simple data base for numeric time series, written purely in R, so no external database-software is needed. Series are stored in plain-text files (the most-portable and enduring file type) in CSV format. Timestamps are encoded using R's native numeric representation for Date'/'POSIXct', which makes them fast to parse, but keeps them accessible with other software. The package provides tools for saving and updating series in this standardised format, for retrieving and joining data, for summarising files and directories, and for coercing series from and to other data types (such as zoo series).

Automates translating the instructions of iatgen generated qsf (Qualtrics survey files) to other languages using either officially supported or user-supplied translations (for tutorial see Santos et al., 2023 <doi:10.17504/protocols.io.kxygx34jdg8j/v1>).

Tidy standardized mean differences ('SMDs'). tidysmd uses the smd package to calculate standardized mean differences for variables in a data frame, returning the results in a tidy format.

Includes functions for mapping named lists to function arguments, random strings, pasting and combining rows together across columns, etc.

Return the first four moments of the SMN distributions (Normal, Student-t, Pearson VII, Slash or Contaminated Normal).

Truncation of univariate probability distributions. The probability distribution can come from other packages so long as the function names follow the standard d, p, q, r naming format. Also other univariate probability distributions are included.

An integrated R interface to several United States Census Bureau APIs (<https://www.census.gov/data/developers/data-sets.html>) and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping and spatial analysis.

This package implements the truncated harmonic mean estimator (THAMES) of the reciprocal marginal likelihood using posterior samples and unnormalized log posterior values via reciprocal importance sampling. Metodiev, Perrot-Dockès, Ouadah, Irons, Latouche, & Raftery (2024). Bayesian Analysis. <doi:10.1214/24-BA1422>.

Create interactive tables, calendars, charts and markdown WYSIWYG editor with TOAST UI <https://ui.toast.com/> libraries to integrate in shiny applications or rmarkdown HTML documents.

It includes functions like tropical addition, tropical multiplication for vectors and matrices. In tropical algebra, the tropical sum of two numbers is their minimum and the tropical product of two numbers is their ordinary sum. For more information see also I. Simon (1988) Recognizable sets with multiplicities in the tropical semi ring: Volume 324 Lecture Notes I Computer Science, pages 107-120 <doi: 10.1007/BFb0017135>.

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>.

Framework to run Monte Carlo simulations over a parameter grid. Allows to parallelize the simulations. Generates plots and LaTeX tables summarizing the results from the simulation.

Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.

This package provides access to datasets, models and preprocessing facilities for deep learning with images. Integrates seamlessly with the torch package and it's API borrows heavily from PyTorch vision package.

Simple toolkit for working with TOML text. Based on tomledit which allows for modifying TOML while preserving order, comments,and whitespace.

This package provides functions for imputing missing item responses for dichotomous and polytomous test and assessment data. This package enables missing imputation methods that are suitable for test and assessment data, including: listwise (LW) deletion (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>), treating as incorrect (IN, see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>; Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>; Pohl et al., 2014 <doi: 10.1177/0013164413504926>), person mean imputation (PM), item mean imputation (IM), two-way (TW) and response function (RF) imputation, (see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>), logistic regression (LR) imputation, predictive mean matching (PMM), and expectationâ maximization (EM) imputation (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).

This is a collection of functions optimized for working with with various kinds of text matrices. Focusing on the text matrix as the primary object - represented either as a base R dense matrix or a Matrix package sparse matrix - allows for a consistent and intuitive interface that stays close to the underlying mathematical foundation of computational text analysis. In particular, the package includes functions for working with word embeddings, text networks, and document-term matrices. Methods developed in Stoltz and Taylor (2019) <doi:10.1007/s42001-019-00048-6>, Taylor and Stoltz (2020) <doi:10.1007/s42001-020-00075-8>, Taylor and Stoltz (2020) <doi:10.15195/v7.a23>, and Stoltz and Taylor (2021) <doi:10.1016/j.poetic.2021.101567>.