Useful to visualize the Poissoneity (an independent Poisson statistical framework, where each RNA measurement for each cell comes from its own independent Poisson distribution) of Unique Molecular Identifier (UMI) based single cell RNA sequencing (scRNA-seq) data, and explore cell clustering based on model departure as a novel data representation.

Simulate age-structured populations that vary in space and time and explore the efficacy of a range of built-in or user-defined sampling protocols to reproduce the population parameters of the known population. (See Regular et al. (2020) <doi:10.1371/journal.pone.0232822> for more details).

Computes sequential A-, MV-, D- and E-optimal or near-optimal block and row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.

This package implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides statistical methods based on the SWDFT, and graphical tools to display the outputs.

This package provides a spectral framework to map quantitative trait loci (QTLs) affecting joint differential networks of gene co-Expression. Test the equivalence among multiple biological networks via spectral statistics. See reference Hu, J., Weber, J. N., Fuess, L. E., Steinel, N. C., Bolnick, D. I., & Wang, M. (2025) <doi:10.1371/journal.pcbi.1012953>.

Settings and functions to extend the knitr Stata engine.

This package provides a Graphical user interface to calculate the rainfall-runoff relation using the Natural Resources Conservation Service - Curve Number method (NRCS-CN method) but include modifications by Hawkins et al., (2002) about the Initial Abstraction. This GUI follows the programming logic of a previously published software (Hernandez-Guzman et al., 2011)<doi:10.1016/j.envsoft.2011.07.006>. It is a raster-based GIS tool that outputs runoff estimates from Land use/land cover and hydrologic soil group maps. This package has already been published in Journal of Hydroinformatics (Hernandez-Guzman et al., 2021)<doi:10.2166/hydro.2020.087> but it is under constant development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo and represents a collaborative effort between the Hydro-Geomatic Lab (INIRENA) with the Environmental Management Lab (CIAD, A.C.).

Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics.

An extension to the individual claim simulator called SynthETIC (on CRAN), to simulate the evolution of case estimates of incurred losses through the lifetime of an insurance claim. The transactional simulation output now comprises key dates, and both claim payments and revisions of estimated incurred losses. An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator" <arXiv:2109.04058>.

The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.

This package provides a pipeline to perform small area estimation and prevalence mapping of binary indicators using health and demographic survey data, described in Fuglstad et al. (2022) <doi:10.48550/arXiv.2110.09576> and Wakefield et al. (2020) <doi:10.1111/insr.12400>.

Design a Bayesian seamless multi-arm biomarker-enriched phase II/III design with the survival endpoint with allowing sample size re-estimation. James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) <doi:10.1038/bjc.2015.278>. Guosheng Yin, Nan Chen, J. Jack Lee (2018) <doi:10.1007/s12561-017-9199-7>. Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) <doi:10.1002/sim.6971>.

In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>.

This package provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not. Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package VCA'. All kinds of, possibly transformed (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.

This package provides functions for the stratigraphic analysis of phylogenetic trees.

This package implements the Simple Non-Iterative Clustering algorithm for superpixel segmentation of multi-band images, as introduced by Achanta and Susstrunk (2017) <doi:10.1109/CVPR.2017.520>. Supports both standard image arrays and geospatial raster objects, with a design that can be extended to other spatial data frameworks. The algorithm groups adjacent pixels into compact, coherent regions based on spectral similarity and spatial proximity. A high-performance implementation supports images with arbitrary spectral bands.

This package provides functions that provide statistical methods for interval-censored (grouped) data. The package supports the estimation of linear and linear mixed regression models with interval-censored dependent variables. Parameter estimates are obtained by a stochastic expectation maximization algorithm. Furthermore, the package enables the direct (without covariates) estimation of statistical indicators from interval-censored data via an iterative kernel density algorithm. Survey and Organisation for Economic Co-operation and Development (OECD) weights can be included into the direct estimation (see, Walter, P. (2019) <doi:10.17169/refubium-1621>).

Enables reading and writing binary and ASCII data to RS232/RS422/RS485 or any other virtual serial interface of the computer.

Construct various types of space-filling designs, including Latin hypercube designs, clustering-based designs, maximin designs, maximum projection designs, and uniform designs (Joseph 2016 <doi:10.1080/08982112.2015.1100447>). It also offers the option to optimize designs based on user-defined criteria. This work is supported by U.S. National Science Foundation grant DMS-2310637.

This package provides kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates. Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) <doi:10.1080/01621459.2014.957289>.

Builds, evaluates and validates a nomogram with survey data and right-censored outcomes. As described in Capanu (2015) <doi:10.18637/jss.v064.c01>, the package contains functions to create the nomogram, validate it using bootstrap, as well as produce the calibration plots.

Ordinary and modified statistics for symmetrical linear regression models with small samples. The supported ordinary statistics include Wald, score, likelihood ratio and gradient. The modified statistics include score, likelihood ratio and gradient. Diagnostic tools associated with the fitted model are implemented. For more details see Medeiros and Ferrari (2017) <DOI:10.1111/stan.12107>.

Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) <doi:10.1111/rssb.12061> for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.

Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.