Secure handling of API keys can be difficult. This package provides secure convenience functions for entering / handling API keys and opening connections via inversion of control on those keys. Works seamlessly between production and developer environments.

This package provides methods for detecting multiple change-points and segmenting univariate time series using Smallest Valid Partitioning (SVP). SVP searches for a partition with the smallest number of segments whose segments satisfy a user-defined or built-in validity test. Among partitions with the same number of segments, it minimizes a within-segment sum-of-squared-errors criterion.

Various functions for discrete time survival analysis and longitudinal analysis. SIMEX method for correcting for bias for errors-in-variables in a mixed effects model. Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation.

It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph or spiral chart. It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns.

This package provides a graphical user interface (GUI) for fitting Bayesian regression models using the package brms which in turn relies on Stan (<https://mc-stan.org/>). The shinybrms GUI is a shiny app.

This package provides a collection of classes and methods for working with times and dates. The code was originally available in S-PLUS'.

Collection of custom input controls and user interface components for Shiny applications. Give your applications a unique and colorful style !

This package provides methods for analysis of energy consumption data (electricity, gas, water) at different data measurement intervals. The package provides feature extraction methods and algorithms to prepare data for data mining and machine learning applications. Deatiled descriptions of the methods and their application can be found in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9> "Enhancing energy efficiency in the residential sector with smart meter data analytics".

This package provides a set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example to export to a PLINK set.

There are four categories of Phase III clinical trials according to different research goals, including (1) Testing for equality, (2) Superiority trial, (3) Non-inferiority trial, and (4) Equivalence trial. This package aims to help researchers to calculate sample size when comparing means or proportions in Phase III clinical trials with different research goals.

This package implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering"; published in Journal of the American Statistical Association 105(490): 713-726.

This package provides a tool for bootstrapping new packages with useful defaults, including a test suite outline that passes checks and helpers for running tests, checking test coverage, building vignettes, and more. Package skeletons it creates are set up for pushing your package to GitHub and using other hosted services for building and test automation.

It contains functions to estimate multivariate Student's t dynamic and static regression models for given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form. Poudyal, N., and Spanos, A. (2022) <doi:10.3390/econometrics10020017>. Spanos, A. (1994) <http://www.jstor.org/stable/3532870>.

Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D) through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of the data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>). This modeling is carried out along a sliding window through the data and yields estimates of the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell formation relative to the annual cycle with their uncertainties. All formulae in the package serve this purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the shell carbonate) through input parameters.

This package implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in C++ using RcppArmadillo and integrated into R via Rcpp modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.

The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.

Fits sparse linear varying coefficient models (VCMs), which assert a linear relationship between an outcome and several covariates that is allowed to change as functions of additional variables known as effect modifiers. Designed for high-dimensional settings where the number of covariates (i.e., number of slopes) is comparable to or larger than the number of observations. Approximates the coefficient functions using a version of Bayesian Additive Regression Trees that can perform global-local shrinkage. For more details see Ghosh, Bhogale, and Deshpande (2026+) <doi:10.48550/arXiv.2510.08204>.

This package provides functions that simplify calls to the Skilljar API. See <https://api.skilljar.com/docs/> for documentation on the Skilljar API. This package is not supported by Skilljar'.

This package provides a facility to generate balanced semi-Latin rectangles with any cell size (preferably up to ten) with given number of treatments, see Uto, N.P. and Bailey, R.A. (2020). "Balanced Semi-Latin rectangles: properties, existence and constructions for block size two". Journal of Statistical Theory and Practice, 14(3), 1-11, <doi:10.1007/s42519-020-00118-3>. It also provides facility to generate partially balanced semi-Latin rectangles for cell size 2, 3 and 4 for any number of treatments.

This package provides an interactive Kanban board widget for shiny applications. It allows users to manage tasks using a drag-and-drop interface and offers customizable styling options. shinykanban is ideal for project management, task tracking, and agile workflows within shiny apps.

This package provides a framework for simulating spatially explicit genomic data which leverages real cartographic information for programmatic and visual encoding of spatiotemporal population dynamics on real geographic landscapes. Population genetic models are then automatically executed by the SLiM software by Haller et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes, using a custom built-in simulation SLiM script. Additionally, fully abstract spatial models not tied to a specific geographic location are supported, and users can also simulate data from standard, non-spatial, random-mating models. These can be simulated either with the SLiM built-in back-end script, or using an efficient coalescent population genetics simulator msprime by Baumdicker et al. (2022) <doi:10.1093/genetics/iyab229> with a custom-built Python script bundled with the R package. Simulated genomic data is saved in a tree-sequence format and can be loaded, manipulated, and summarised using tree-sequence functionality via an R interface to the Python module tskit by Kelleher et al. (2019) <doi:10.1038/s41588-019-0483-y>. Complete model configuration, simulation and analysis pipelines can be therefore constructed without a need to leave the R environment, eliminating friction between disparate tools for population genetic simulations and data analysis.

This package creates superpixels based on input spatial data. This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters). It is based on the SLIC algorithm (Achanta et al. (2012) <doi:10.1109/TPAMI.2012.120>), and readapts it to work with arbitrary dissimilarity measures.

This package provides a small set of helper functions to convert sjPlot HTML-tables to R data.frame objects / knitr::kable-tables.

An R interface to the Python sqlfluff SQL linter and formatter via the reticulate package. Enables linting, fixing, and parsing of SQL queries with support for multiple dialects. Includes special handling for glue SQL syntax with curly-brace placeholders.