Empirical likelihood methods for asymptotically efficient estimation of models based on conditional or unconditional moment restrictions; see Kitamura, Tripathi & Ahn (2004) <doi:10.1111/j.1468-0262.2004.00550.x> and Owen (2013) <doi:10.1002/cjs.11183>. Kernel-based non-parametric methods for density/regression estimation and numerical routines for empirical likelihood maximisation are implemented in Rcpp for speed.

Semiparametric empirical likelihood ratio based tests of change-point with one-change or epidemic alternatives with data-based model diagnostic are contained.

Sparse redundancy analysis for high dimensional (biomedical) data. Directional multivariate analysis to express the maximum variance in the predicted data set by a linear combination of variables of the predictive data set. Implemented in a partial least squares framework, for more details see Csala et al. (2017) <doi:10.1093/bioinformatics/btx374>.

This package provides functions for making particle-size analysis. Sieve tests are widely used to obtain particle-size distribution of powders or granular materials.

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>.

Graphs (or networks) and graph component calculations for spatial locations in 1D, 2D, 3D etc.

This package provides a supervised compression method that incorporates the response for reducing big data to a carefully selected subset. Please see Joseph and Mak (2021) <doi:10.1002/sam.11508>. This research is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646.

This package provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like CellChat <http://www.cellchat.org/>, CellPhoneDB <https://www.cellphonedb.org/>, Cellinker <https://www.rna-society.org/cellinker/index.html>, ICELLNET <https://github.com/soumelis-lab/ICELLNET>, and ConnectomeDB <https://humanconnectome.org/software/connectomedb/> to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales.

This package provides functions for efficiently estimating properties of the Van Genuchten-Mualem model for soil hydraulic parameters from possibly sparse soil water retention and hydraulic conductivity data by multi-response parameter estimation methods (Stewart, W.E., Caracotsios, M. Soerensen, J.P. (1992) "Parameter estimation from multi-response data" <doi:10.1002/aic.690380502>). Parameter estimation is simplified by exploiting the fact that residual and saturated water contents and saturated conductivity are conditionally linear parameters (Bates, D. M. and Watts, D. G. (1988) "Nonlinear Regression Analysis and Its Applications" <doi:10.1002/9780470316757>). Estimated parameters are optionally constrained by the evaporation characteristic length (Lehmann, P., Bickel, S., Wei, Z. and Or, D. (2020) "Physical Constraints for Improved Soil Hydraulic Parameter Estimation by Pedotransfer Functions" <doi:10.1029/2019WR025963>) to ensure that the estimated parameters are physically valid. Common S3 methods and further utility functions allow to process, explore and visualise estimation results.

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

This package provides methods for fitting bivariate lines in allometry using the major axis (MA) or standardised major axis (SMA), and for making inferences about such lines. The available methods of inference include confidence intervals and one-sample tests for slope and elevation, testing for a common slope or elevation amongst several allometric lines, constructing a confidence interval for a common slope or elevation, and testing for no shift along a common axis, amongst several samples. See Warton et al. 2012 <doi:10.1111/j.2041-210X.2011.00153.x> for methods description.

This package provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2024) <doi:10.48550/arXiv.2410.09504>. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage C++ for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.

The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation.

This package provides a statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA <doi:10.1093/jamia/ocaa079> for details.

Perform spatial temporal analysis of moving polygons; a longstanding analysis problem in Geographic Information Systems. Facilitates directional analysis, distance analysis, and some other simple functionality for examining spatial-temporal patterns of moving polygons.

This package implements the SoftBart model of described by Linero and Yang (2018) <doi:10.1111/rssb.12293>, with the optional use of a sparsity-inducing prior to allow for variable selection. For usability, the package maintains the same style as the BayesTree package.

This package provides functions to perform most of the common analysis in genome association studies are implemented. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated. The methods are described in Gonzalez JR et al., 2007 <doi: 10.1093/bioinformatics/btm025>.

It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.

This package provides functions that wrap HTML Bootstrap components code to enable the design and layout of informative landing home pages for Shiny applications. This can lead to a better user experience for the users and writing less HTML for the developer.

Launch an application by a simple click without opening R or RStudio. The package has 3 functions of which only one is essential in its use, `shiny.exe()`. It generates a script in the open shiny project then create a shortcut in the same folder that allows you to launch the app by clicking.If you set `host = public'`, the application will be launched on the public server to which you are connected. Thus, all other devices connected to the same server will be able to access the application through the link of your `IPv4` extended by the port. You can stop the application by leaving the terminal opened by the shortcut.

Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205> and Peressini and Sullivan (1988, ISBN:0-387-96614-5).

Transformation of sea currents to connectivity data. Two files of horizontal and vertical currents flows are transformed into connectivity data in the form of sfnetwork', shapefile, edge list and adjacency matrix. An application example is shown at Nagkoulis et al. (2025) <doi:10.1016/j.dib.2024.111268>.

An input controller for R Shiny: a matrix with radio buttons, where only one option per row can be selected.

In base R, object attributes are lost when objects are modified by common data operations such as subset, filter, slice, append, extract etc. This packages allows objects to be marked as sticky and have attributes persisted during these operations or when inserted into or extracted from list-like or table-like objects.