This package provides a SAS interface, through SASPy'(<https://sassoftware.github.io/saspy/>) and reticulate'(<https://rstudio.github.io/reticulate/>). This package helps you create SAS sessions, execute SAS code in remote SAS servers, retrieve execution results and log, and exchange datasets between SAS and R'. It also helps you to install SASPy and create a configuration file for the connection. Please review the SASPy license file as instructed so that you comply with its separate and independent license.

Load and export SomaScan data via the Standard BioTools, Inc. structured text file called an ADAT ('*.adat'). For file format see <https://github.com/SomaLogic/SomaLogic-Data/blob/main/README.md>. The package also exports auxiliary functions for manipulating, wrangling, and extracting relevant information from an ADAT object once in memory.

This package provides a time input widget for Shiny. This widget allows intuitive time input in the [hh]:[mm]:[ss] or [hh]:[mm] (24H) format by using a separate numeric input for each time component. The interface with R uses date-time objects. See the project page for more information and examples.

Compute relative or absolute population trends across space and time using predictions from models fitted to ecological population abundance data, as described in Knape (2025) <doi:10.1016/j.ecolind.2025.113435>. The package supports models fitted by mgcv or brms', and draws from posterior predictive distributions.

Sequential Kalman filter for scalable online changepoint detection by temporally correlated data. It enables fast single and multiple change points with missing values. See the reference: Hanmo Li, Yuedong Wang, Mengyang Gu (2023), <arXiv:2310.18611>.

I provide functions to calculate Gross Primary Productivity, Net Ecosystem Production, and Ecosystem Respiration from single station diurnal Oxygen curves.

This package implements a group-bridge penalized function-on-scalar regression model proposed by Wang et al. (2023) <doi:10.1111/biom.13684>, to simultaneously estimate functional coefficient and recover the local sparsity.

An interface to access data from Substack publications via API. Users can fetch the latest, top, search for specific posts, or retrieve a single post by its slug. This functionality is useful for developers and researchers looking to analyze Substack content or integrate it into their applications. For more information, visit the API documentation at <https://substackapi.dev/introduction>.

Create mixed models with repeated measures using natural cubic splines applied to an observed continuous time variable, as described by Donohue et al. (2023) <doi:10.1002/pst.2285>. Iterate through multiple covariance structure types until one converges. Categorize observed time according to scheduled visits. Perform subgroup analyses.

Function for the GUI API to interact with external IDE/code editors.

This package implements the Stratigraphic Plug Alignment (SPA) procedure for integrating sparsely sampled plug-based measurements (e.g., total organic carbon, porosity, mineralogy) with high-resolution X-ray fluorescence (XRF) geochemical data. SPA uses linear interpolation via the base approx() function with constrained extrapolation (rule = 1) to preserve stratigraphic order and avoid estimation beyond observed depths. The method aligns all datasets to a common depth grid, enabling high-resolution multivariate analysis and stratigraphic interpretation of core-based datasets such as those from the Utica and Point Pleasant formations. See R Core Team (2025) <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/stats-package.html> and Omodolor (2025) <http://rave.ohiolink.edu/etdc/view?acc_num=case175262671767524> for methodological background and geological context.

The nonparametric trend and its derivatives in equidistant time series (TS) with short-memory stationary errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel smoother is also built-in as a comparison. With version 1.1.0, a linearity test for the trend function, forecasting methods and backtesting approaches are implemented as well. The smoothing methods of the package are described in Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.

Import, create and assemble data needed to fit spatial-statistical stream-network models using the SSN2 package for R'. Streams, observations, and prediction locations are represented as simple features and specific tools provided to define topological relationships between features; calculate the hydrologic distances (with flow-direction preserved) and the spatial additive function used to weight converging stream segments; and export the topological, spatial, and attribute information to an `SSN` (spatial stream network) object, which can be efficiently stored, accessed and analysed in R'. A detailed description of methods used to calculate and format the spatial data can be found in Peterson, E.E. and Ver Hoef, J.M., (2014) <doi:10.18637/jss.v056.i02>.

Computes the effective range of a smoothing matrix, which is a measure of the distance to which smoothing occurs. This is motivated by the application of spatial splines for adjusting for unmeasured spatial confounding in regression models, but the calculation of effective range can be applied to smoothing matrices in other contexts. For algorithmic details, see Rainey and Keller (2024) "spconfShiny: an R Shiny application..." <doi:10.1371/journal.pone.0311440> and Keller and Szpiro (2020) "Selecting a Scale for Spatial Confounding Adjustment" <doi:10.1111/rssa.12556>.

Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions.

With satin functions, visualisation, data extraction and further analysis like producing climatologies from several images, and anomalies of satellite derived ocean data can be easily done. Reading functions can import a user defined geographical extent of data stored in netCDF files. Currently supported ocean data sources include NASA's Oceancolor web page <https://oceancolor.gsfc.nasa.gov/>, sensors VIIRS-SNPP; MODIS-Terra; MODIS-Aqua; and SeaWiFS. Available variables from this source includes chlorophyll concentration, sea surface temperature (SST), and several others. Data sources specific for SST that can be imported too includes Pathfinder AVHRR <https://www.ncei.noaa.gov/products/avhrr-pathfinder-sst> and GHRSST <https://www.ghrsst.org/>. In addition, ocean productivity data produced by Oregon State University can also be handled previous conversion from HDF4 to HDF5 format. Many other ocean variables can be processed by importing netCDF data files from two European Union's Copernicus Marine Service databases <https://marine.copernicus.eu/>, namely Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast.

This package provides a few major genes and a series of polygene are responsive for each quantitative trait. Major genes are individually identified while polygene is collectively detected. This is mixed major genes plus polygene inheritance analysis or segregation analysis (SEA). In the SEA, phenotypes from a single or multiple bi-parental segregation populations along with their parents are used to fit all the possible models and the best model of the trait for population phenotypic distributions is viewed as the model of the trait. There are fourteen types of population combinations available. Zhang Yuan-Ming, Gai Jun-Yi, Yang Yong-Hua (2003, <doi:10.1017/S0016672303006141>).

This package provides a function for the estimation of parameters in a binary regression with the skew-probit link function. Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented, as described in DongHyuk Lee and Samiran Sinha (2019+) <doi:10.1080/00949655.2019.1590579>.

Simple result caching in R based on R.cache. The global environment is not considered when caching results simplifying moving files between multiple instances of R. Relies on more base functions than R.cache (e.g. cached results are saved using saveRDS() and readRDS()).

Network meta-analysis for survival outcome data often involves several studies only involve dichotomized outcomes (e.g., the numbers of event and sample sizes of individual arms). To combine these different outcome data, Woods et al. (2010) <doi:10.1186/1471-2288-10-54> proposed a Bayesian approach using complicated hierarchical models. Besides, frequentist approaches have been alternative standard methods for the statistical analyses of network meta-analysis, and the methodology has been well established. We proposed an easy-to-implement method for the network meta-analysis based on the frequentist framework in Noma and Maruo (2025) <doi:10.1101/2025.01.23.25321051>. This package involves some convenient functions to implement the simple synthesis method.

Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs.

This package provides convenience functions to replace hyphen-minuses (ASCII 45) with proper minus signs (Unicode character 2212). The true minus matches the plus symbol in width, line thickness, and height above the baseline. It was designed for mathematics, looks better in presentation, and is understood properly by screen readers.

This package provides a set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016)<https://www.researchgate.net/publication/316517889_S-GSTAR-SUR_model_for_seasonal_spatio_temporal_data_forecasting>.

Implementation of various methods in estimation of species richness or diversity in Wang (2011)<doi:10.18637/jss.v040.i09>.