This package implements a two-stage estimation approach for Cox regression using five-parameter M-spline functions to model the baseline hazard. It allows for flexible hazard shapes and model selection based on log-likelihood criteria as described in Teranishi et al.(2025). In addition, the package provides functions for constructing and evaluating B-spline copulas based on five M-spline or I-spline basis functions, allowing users to flexibly model and compute bivariate dependence structures. Both the copula function and its density can be evaluated. Furthermore, the package supports computation of dependence measures such as Kendall's tau and Spearman's rho, derived analytically from the copula parameters.

Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site <https://landsat.gsfc.nasa.gov/>. By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery.

This package provides a graphical user interface for cross-sectional network modeling with the statnet software suite <https://github.com/statnet>.

Proposes a torch implementation of Graph Net architecture allowing different options for message passing and feature embedding.

Created for population health analytics and monitoring. The functions in this package work best when working with patient level Master Patient Index-like datasets . Built to be used by NHS bodies and other health service providers.

Estimating the Shapley values using the algorithm in the paper Liuqing Yang, Yongdao Zhou, Haoda Fu, Min-Qian Liu and Wei Zheng (2024) <doi:10.1080/01621459.2023.2257364> "Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs". You provide the data and define the value function, it retures the estimated Shapley values based on sampling methods or experimental designs.

Median-of-means is a generic yet powerful framework for scalable and robust estimation. A framework for Bayesian analysis is called M-posterior, which estimates a median of subset posterior measures. For general exposition to the topic, see the paper by Minsker (2015) <doi:10.3150/14-BEJ645>.

This package provides functions to produce a fully fledged geo-spatial object extent as a SpatialPolygonsDataFrame'. Also included are functions to generate polygons from raster data using quadmesh techniques, a round number buffered extent, and general spatial-extent and raster-like extent helpers missing from the originating packages. Some latitude-based tools for polar maps are included.

Based on the structure of the SPSS version of the Swiss Household Panel (SHP) data, provides a function seqFromWaves() that seeks the data of variables specified by the user in each of the wave files and collects them as sequences. The function also matches the sequences with variables from other files such as the master files of persons (MP) and households (MH), and social origins (SO). It can also match with activity calendar data (CA).

This package provides a system enables cross study Analysis by extracting and filtering study data for control animals from CDISC SEND Study Repository. These data types are supported: Body Weights, Laboratory test results and Microscopic findings. These database types are supported: SQLite and Oracle'.

This package provides access to granular sub-national income data from the MCC-PIK Database Of Sub-national Economic Output (DOSE). The package downloads and processes the data from its open repository on Zenodo (<https://zenodo.org/records/13773040>). Functions are provided to fetch data at multiple geographic levels, match coordinates to administrative regions, and access associated geometries.

This package provides a pipeline for estimating the stellar age, mass, and radius given observational effective temperature, [Fe/H], and astroseismic parameters. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models, as described in Valle et al. (2014) <doi:10.1051/0004-6361/201322210>.

Convert laboratory data to the Portuguese Information System for Water Resources SNIRH file format. SNIRH is Portugal's national water resources information system <https://snirh.apambiente.pt/>. The package validates station data, converts parameters and units, and generates compliant output files for data submission.

Provide utilities to work with solar time, i.e. where noon is exactly when sun culminates. Provides functions for computing sun position and times of sunrise and sunset.

Minimal R client for the Screenshotbase API to render website screenshots and query account status. Provides functions to set the API key, call the status endpoint, and take a screenshot as a raw image response.

An iterative feature selection method that internally utilizes various Machine Learning methods that have embedded feature reduction in order to shrink down the feature space into a small and yet robust set.

Scripts and exercises that use card shuffling to teach confidence interval comparisons for different estimators.

This gadget allows you to use the recipes package belonging to tidymodels to carry out the data preprocessing tasks in an interactive way. Build your recipe by dragging the variables, visually analyze your data to decide which steps to use, add those steps and preprocess your data.

This package provides flexible hazard ratio curves allowing non-linear relationships between continuous predictors and survival. To better understand the effects that each continuous covariate has on the outcome, results are expressed in terms of hazard ratio curves, taking a specific covariate value as reference. Confidence bands for these curves are also derived.

RegLog system provides a set of shiny modules to handle register procedure for your users, alongside with login, edit credentials and password reset functionality. It provides support for popular SQL databases and optionally googlesheet-based database for easy setup. For email sending it provides support for emayili and gmailr backends. Architecture makes customizing usability pretty straightforward. The authentication system created with shiny.reglog is designed to be optional: user don't need to be logged-in to access your application, but when logged-in the user data can be used to read from and write to relational databases.

Uses C++ via the Rcpp package to parse modern Excel files ('.xlsx'). Memory usage is kept minimal by decompressing only parts of the file at a time, while employing multiple threads to achieve significant runtime reduction. Uses <https://github.com/richgel999/miniz> and <https://github.com/lemire/fast_double_parser>.

This package provides tools to import and export from several existing pieces of ion-channel analysis software such as TAC', QUB', SCAN', and Clampfit', implements procedures such as dwell-time correction and defining bursts with a critical time, and provides tools for analysis of bursts, such as tools for sorting and plotting.

This package provides predictive accuracy tools to evaluate time-to-event survival models. This includes calculating the concordance probability estimate that incorporates the follow-up time for a particular study developed by Devlin, Gonen, Heller (2020)<doi:10.1007/s10985-020-09503-3>. It also evaluates the concordance probability estimate for nested Cox proportional hazards models using a projection-based approach by Heller and Devlin (under review).

This package provides a set of functions to quantify and visualise social autocorrelation.