This package provides Stable Diffusion image generation using the ggmlR library, with no Python or external API dependencies. Supports text-to-image and image-to-image generation for SD 1.x, SD 2.x, SDXL', Flux, and FLUX.2'. A single sd_generate() function handles the entire pipeline, including sampling and high-resolution output. Features multi-GPU support, a Shiny GUI, and runs on CPU or Vulkan GPU across Linux, macOS, and Windows.

Data related to the Salem Witch Trials Datasets and tutorials documenting the witch accusations and trials centered around Salem, Massachusetts in 1692. Originally assembled by Richard B. Latner of Tulane University for his website <https://www2.tulane.edu/~salem/index.html>. The data sets include information on 152 accused witches, members of the Salem Village Committee, signatories of petitions related to the events, and tax data for Salem Village.

This package provides simple and powerful interfaces that facilitate interaction with ODBC data sources. Each data source gets its own unique and dedicated interface, wrapped around RODBC'. Communication settings are remembered between queries, and are managed silently in the background. The interfaces support multi-statement SQL scripts, which can be parameterised via metaprogramming structures and embedded R expressions.

An ADMM implementation of SDP-1, a semidefinite programming relaxation of the maximum likelihood estimator for fitting a block model. SDP-1 has a tendency to produce equal-sized blocks and is ideal for producing a form of network histogram approximating a nonparametric graphon model. Alternatively, it can be used for community detection. (This is experimental code, proceed with caution.).

Data wrangling and cleaning, quality control checks and implementation of machine learning classification algorithm.

This package provides functions for creating, displaying, and evaluating stopping rules for safety monitoring in clinical studies.

Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal statistic for inference through random scaling. The methodology used in the SGDinference package is described in detail in the following papers: (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) <doi:10.1609/aaai.v36i7.20701> "Fast and robust online inference with stochastic gradient descent via random scaling". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) <arXiv:2209.14502> "Fast Inference for Quantile Regression with Tens of Millions of Observations".

Monte Carlo simulations of a game-theoretic model for the legal exemption system of the European cartel law are implemented in order to estimate the (mean) deterrent effect of this system. The input and output parameters of the simulated cartel opportunities can be visualized by three-dimensional projections. A description of the model is given in Moritz et al. (2018) <doi:10.1515/bejeap-2017-0235>.

Models with skewâ normally distributed and thus asymmetric error terms, implementing the methods developed in Badunenko and Henderson (2023) "Production analysis with asymmetric noise" <doi:10.1007/s11123-023-00680-5>. The package provides tools to estimate regression models with skewâ normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms.

Multi-generational pedigree inference from incomplete data on hundreds of SNPs, including parentage assignment and sibship clustering. See Huisman (2017) (<DOI:10.1111/1755-0998.12665>) for more information.

Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) <doi:10.7717/peerj.9593> for details.

This package performs Stratified Covariate Balancing with Markov blanket feature selection and use of synthetic cases. See Alemi et al. (2016) <DOI:10.1111/1475-6773.12628>.

Standardized accuracy (staccuracy) is a framework for expressing accuracy scores such that 50% represents a reference level of performance and 100% is a perfect prediction. The staccuracy package provides tools for creating staccuracy functions as well as some recommended staccuracy measures. It also provides functions for some classic performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUCROC), as well as their winsorized versions when applicable.

This package provides a fast and accurate pipeline for single-cell analyses. The scDHA software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).

This package provides a comprehensive framework for quantifying the fundamental thermodynamic parameters of adsorption reactionsâ changes in the standard Gibbs free energy (delta G), enthalpy (delta H), and entropy (delta S)â is essential for understanding the spontaneity, heat effects, and molecular ordering associated with sorption processes. By analysing temperature-dependent equilibrium data, thermodynamic interpretation expands adsorption studies beyond conventional isotherm fitting, offering deeper insight into underlying mechanisms and surfaceâ solute interactions. Such an approach typically involves evaluating equilibrium coefficients across multiple temperatures and non-temperature treatments, deriving thermodynamic parameters using established thermodynamic relationships, and determining delta G as a temperature-specific indicator of adsorption favourability. This analytical pathway is widely applicable across environmental science, soil science, chemistry, materials science, and engineering, where reliable assessment of sorption behaviour is critical for examining contaminant retention, nutrient dynamics, and the behaviour of natural and engineered surfaces. By focusing specifically on thermodynamic inference, this framework complements existing adsorption isotherm-fitting packages such as âAdIsMFâ <https://CRAN.R-project.org/package=AdIsMF> <doi:10.32614/CRAN.package.AdIsMF>, and strengthens the scientific basis for interpreting adsorption energetics in both research and applied contexts. Details can be found in Roy et al. (2025) <doi:10.1007/s11270-025-07963-7>.

This package performs support vectors analysis for data sets with survival outcome. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in the same model.

This package provides a shiny application estimating the operating characteristics of the Student's t-test by Student (1908) <doi:10.1093/biomet/6.1.1>, Welch's t-test by Welch (1947) <doi:10.1093/biomet/34.1-2.28>, and Wilcoxon test by Wilcoxon (1945) <doi:10.2307/3001968> in one-sample or two-sample cases, in settings defined by the user (conditional distribution, sample size per group, location parameter per group, nuisance parameter per group), using Monte Carlo simulations Malvin H. Kalos, Paula A. Whitlock (2008) <doi:10.1002/9783527626212>.

More easy to get intersection, union or complementary set and combinations.

Generate the same random numbers in R and Python.

Computes the extended spring indices (SI-x) and false spring exposure indices (FSEI). The SI-x indices are standard indices used for analysis in spring phenology studies. In addition, the FSEI is also from research on the climatology of false springs and adjusted to include an early and late false spring exposure index. The indices include the first leaf index, first bloom index, and false spring exposure indices, along with all calculations for all functions needed to calculate each index. The main function returns all indices, but each function can also be run separately. Allstadt et al. (2015) <doi: 10.1088/1748-9326/10/10/104008> Ault et al. (2015) <doi: 10.1016/j.cageo.2015.06.015> Peterson and Abatzoglou (2014) <doi: 10.1002/2014GL059266> Schwarz et al. (2006) <doi: 10.1111/j.1365-2486.2005.01097.x> Schwarz et al. (2013) <doi: 10.1002/joc.3625>.

This package performs non-parametric tests of parametric specifications. Five tests are available. Specific bandwidth and kernel methods can be chosen along with many other options. Allows parallel computing to quickly compute p-values based on the bootstrap. Methods implemented in the package are H.J. Bierens (1982) <doi:10.1016/0304-4076(82)90105-1>, J.C. Escanciano (2006) <doi:10.1017/S0266466606060506>, P.L. Gozalo (1997) <doi:10.1016/S0304-4076(97)86571-2>, P. Lavergne and V. Patilea (2008) <doi:10.1016/j.jeconom.2007.08.014>, P. Lavergne and V. Patilea (2012) <doi:10.1198/jbes.2011.07152>, J.H. Stock and M.W. Watson (2006) <doi:10.1111/j.1538-4616.2007.00014.x>, C.F.J. Wu (1986) <doi:10.1214/aos/1176350142>, J. Yin, Z. Geng, R. Li, H. Wang (2010) <https://www.jstor.org/stable/24309002> and J.X. Zheng (1996) <doi:10.1016/0304-4076(95)01760-7>.

Econometric estimation of simultaneous systems of linear and nonlinear equations using Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS) as suggested, e.g., by Zellner (1962) <doi:10.2307/2281644>, Zellner and Theil (1962) <doi:10.2307/1911287>, and Schmidt (1990) <doi:10.1016/0304-4076(90)90127-F>.

Includes bases for litholog generation: graphical functions based on R base graphics, interval management functions and svg importation functions among others. Also include stereographic projection functions, and other functions made to deal with large datasets while keeping options to get into the details of the data. When using for publication please cite Sebastien Wouters, Anne-Christine Da Silva, Frederic Boulvain and Xavier Devleeschouwer, 2021. The R Journal 13:2, 153-178. The palaeomagnetism functions are based on: Tauxe, L., 2010. Essentials of Paleomagnetism. University of California Press. <https://earthref.org/MagIC/books/Tauxe/Essentials/>; Allmendinger, R. W., Cardozo, N. C., and Fisher, D., 2013, Structural Geology Algorithms: Vectors & Tensors: Cambridge, England, Cambridge University Press, 289 pp.; Cardozo, N., and Allmendinger, R. W., 2013, Spherical projections with OSXStereonet: Computers & Geosciences, v. 51, no. 0, p. 193 - 205, <doi: 10.1016/j.cageo.2012.07.021>.

This package provides functions to nonparametrically assess assumptions sufficient to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of conditional mean functions, and non-negative residual treatment effect. Details are described in: Hsiao E, Tian L, and Parast L (2026). "Avoiding the surrogate paradox: an empirical framework for assessing assumptions." Journal of Nonparametric Statistics <doi:10.1080/10485252.2025.2498609>. There are also functions to assess resilience to the surrogate paradox via calculation of the resilience probability, the resilience bound, and the resilience set. Details will be available in Hsiao E, Tian L, and Parast L, "Resilience Measures for the Surrogate Paradox" (Under Review). Lastly, there is a function to assess resilience to the surrogate paradox in the met-analytic setting, described in Hsiao E and Parast L, "A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience" (Under Review). A tutorial for this package can be found at <https://www.laylaparast.com/surrogateparadoxtest>.