This package produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.

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.

Allows the user to animate shiny elements when scrolling to view them. The animations are activated using the scrollrevealjs library. See <https://scrollrevealjs.org/> for more information.

Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields.

This package implements a method to combine multiple levels of multiple sequence alignment to uncover the structure of complex DNA rearrangements.

Computes the entire regularization path for the two-class svm classifier with essentially the same cost as a single SVM fit.

This package provides functions for constructing mathematical models of dynamical systems from measured input-output data.

R language bindings for SolveBio's API. SolveBio is a biomedical knowledge hub that enables life science organizations to collect and harmonize the complex, disparate "multi-omic" data essential for today's R&D and BI needs.

Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

An Object-oriented Framework for Geostatistical Modeling in S+ containing functions for variogram estimation, variogram fitting and kriging as well as some plot functions. Written entirely in S, therefore works only for small data sets in acceptable computing time.

Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. Trajectories can be analysed to identify groups of important frames. Output visualisation can be generated for maps and pathways. Methodological details can be found in Motta S et al (2022) <doi:10.1021/acs.jctc.1c01163>. I/O functions for xtc format files were implemented using the xdrfile library available under open source license. The relevant information can be found in inst/COPYRIGHT.

Monitoring reporting rates of subject-level clinical events (e.g. adverse events, protocol deviations) reported by clinical trial sites is an important aspect of risk-based quality monitoring strategy. Sites that are under-reporting or over-reporting events can be detected using bootstrap simulations during which patients are redistributed between sites. Site-specific distributions of event reporting rates are generated that are used to assign probabilities to the observed reporting rates. (Koneswarakantha 2024 <doi:10.1007/s43441-024-00631-8>).

An efficient implementation of Scalable Bayesian Rule Lists Algorithm, a competitor algorithm for decision tree algorithms; see Hongyu Yang, Cynthia Rudin, Margo Seltzer (2017) <https://proceedings.mlr.press/v70/yang17h.html>. It builds from pre-mined association rules and have a logical structure identical to a decision list or one-sided decision tree. Fully optimized over rule lists, this algorithm strikes practical balance between accuracy, interpretability, and computational speed.

This app enables interactive validation, interpretation and visualization of structural topic models from the stm package by Roberts and others (2014) <doi:10.1111/ajps.12103>. It also includes helper functions for model diagnostics and extracting data from effect estimates.

This package provides a collection of functions for reading soil data from U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and National Cooperative Soil Survey (NCSS) databases.

This package provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) <doi:10.2307/2532003>, the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) <doi:10.1093/biostatistics/kxl008> and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) <doi:10.1214/aos/1043351257>.

This package provides a stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.

Visualize and tabulate single-choice, multiple-choice, matrix-style questions from survey data. Includes ability to group cross-tabulations, frequency distributions, and plots by categorical variables and to integrate survey weights. Ideal for quickly uncovering descriptive patterns in survey data.

Compute centrographic statistics (central points, standard distance, standard deviation ellipse, standard deviation box) for observations taken at point locations in 2D or 3D. The sfcentral library was inspired in aspace package but conceived to be used in a spatial tidyverse context.

This package provides a set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.

Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.

Calculates sample size for various scenarios, such as sample size to estimate population proportion with stated absolute or relative precision, testing a single proportion with a reference value, to estimate the population mean with stated absolute or relative precision, testing single mean with a reference value and sample size for comparing two unpaired or independent means, comparing two paired means, the sample size For case control studies, estimating the odds ratio with stated precision, testing the odds ratio with a reference value, estimating relative risk with stated precision, testing relative risk with a reference value, testing a correlation coefficient with a specified value, etc. <https://www.academia.edu/39511442/Adequacy_of_Sample_Size_in_Health_Studies#:~:text=Determining%20the%20sample%20size%20for,may%20yield%20statistically%20inconclusive%20results.>.

Interface to Sudachi <https://github.com/WorksApplications/Sudachi>, a Japanese morphological analyzer. This is a port of what is available in Python.

Provision of the S4 SpatialGraph class built on top of objects provided by igraph and sp packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg().