This package provides a framework for visualizing and exploring results of a Management Strategy Evaluation (MSE). The publication quality figures and tables can be developed directly from the R console, or interactively explored with the Slick App. For more details, see the Slick website <https://slick.bluematterscience.com>.

This package provides a comprehensive toolkit for Statistical Process Control (SPC) that combines the rigor of classical Shewhart methodology with modern tidyverse-native interfaces. Provides classical control charts for variables (I-MR, Xbar-R, Xbar-S) and attributes (p, np, c, u), as well as regression-based control charts for processes with trend. Includes Nelson runs tests, Average Run Length (ARL) simulation, process capability indices with bootstrap confidence intervals, Box-Cox transformation guidance, and a clean Phase I / Phase II workflow. All chart objects integrate with broom via tidy', glance and augment methods. References: Shewhart (1931, ISBN:0-87389-076-0); Montgomery (2019, ISBN:978-1-119-39930-8); Nelson (1984) <doi:10.1080/00224065.1984.11978921>; Woodall (2000) <doi:10.1080/00224065.2000.11980013>; Box & Cox (1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>.

This package provides a fast implementation of the SWAG algorithm for Generalized Linear Models which allows to perform a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures.

This package provides most of the data files used in the textbook "Scientific Research and Methodology" by Dunn (2025, ISBN: 9781032496726).

The HJ-Biplot is a multivariate method that represents high-dimensional data in a low-dimensional subspace, capturing most of the informationâ s variability in just a few dimensions. This package implements three new regularized versions of the HJ-Biplot: Ridge, LASSO, and Elastic Net. These versions introduce restrictions that shrink or zero-out variable weights to improve interpretability based on regularization theory. All methods provide graphical representations using ggplot2'.

Basic and model-based soil physical analyses.

Efficiently estimate shape parameters of periodic time series imagery with which a statistical seasonal trend analysis (STA) is subsequently performed. STA output can be exported in conventional raster formats. Methods to visualize STA output are also implemented as well as the calculation of additional basic statistics. STA is based on (R. Eastman, F. Sangermano, B. Ghimire, H. Zhu, H. Chen, N. Neeti, Y. Cai, E. Machado and S. Crema, 2009) <doi:10.1080/01431160902755338>.

High level management of widgets, windows and other graphical resources.

The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.

Shiny wrappers for the RGL package. This package exposes RGL's ability to export WebGL visualization in a shiny-friendly format.

An updated and extended version of spm package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.

Implementation of sequential trial emulation for the analysis of observational databases. The SEQTaRget software accommodates time-varying treatments and confounders, as well as binary and failure time outcomes. SEQTaRget allows to compare both static and dynamic strategies, can be used to estimate observational analogs of intention-to-treat and per-protocol effects, and can adjust for potential selection bias induced by losses-to-follow-up. (Paper to come).

This is a collection of various kinds of data with broad uses for teaching. My students, and academics like me who teach the same topics I teach, should find this useful if their teaching workflow is also built around the R programming language. The applications are multiple but mostly cluster on topics of statistical methodology, international relations, and political economy.

Generalized framework for data generation, Maximum Likelihood Estimation, and Bayesian estimation of stress-strength reliability R = P(Y < X) for arbitrary continuous distributions under censoring schemes based on Chapter 9 of Balakrishnan', Cramer', and Kundu (2023) <ISBN:978-0-12-398387-9>. Users provide probability density functions, cumulative distribution functions, survival functions, support bounds, parameter ranges, and sample sizes. Implements data generation under Type-I, Type-II, progressive Type-II, Type-I hybrid, Type-II hybrid, generalized hybrid, progressive hybrid, joint, block random, middle, and truncation censoring schemes, accompanied by diagnostic histograms, dot plots, and autocorrelation plots. Maximum Likelihood Estimation supports optimization routines including Newton-Raphson', Broyden'-'Fletcher'-'Goldfarb'-'Shanno ('BFGS'), BFGS in R ('BFGSR'), Berndt'-'Hall'-'Hall'-'Hausman ('BHHH'), Simulated Annealing ('SANN'), Conjugate Gradients ('CG'), and Nelder'-'Mead ('NM'), returning summaries ('AIC', coef', logLik', nIter', stdEr', summary, vcov'). Bayesian estimation of stress-strength reliability R = P(Y < X) is performed via Gibbs sampling, Metropolis-Hastings algorithm, Importance Sampling, and Lindley approximation (1980). Methods and censoring schemes are described in Balakrishnan', Cramer', and Kundu (2023, ISBN:978-0-12-398387-9), Lindley (1980) <doi:10.1111/j.2517-6161.1980.tb01102.x>, Geweke (1989) <doi:10.2307/2290062>, Metropolis (1953) <doi:10.1063/1.1699114>, Hastings (1970) <doi:10.1093/biomet/57.1.97>, Geman and Geman (1984) <doi:10.1109/TPAMI.1984.4767596>, Kundu and Gupta (2005) <doi:10.1016/j.jspi.2004.09.006>, Kundu and Gupta (2006) <doi:10.1016/j.csda.2005.02.007>, Berndt', Hall', Hall', and Hausman (1974) <doi:10.3386/t0003>, Fletcher (1987, ISBN:978-0-471-91547-8), and Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>.

Stores objects (e.g. neural networks) that are needed for using Sojourn accelerometer methods. For more information, see Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014) <doi:10.1249/MSS.0b013e3182a42a2d>, Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016) <doi:10.1249/MSS.0000000000000915>, and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.

This package provides a set of functions allowing to implement the SpiceFP approach which is iterative. It involves transformation of functional predictors into several candidate explanatory matrices (based on contingency tables), to which relative edge matrices with contiguity constraints are associated. Generalized Fused Lasso regression are performed in order to identify the best candidate matrix, the best class intervals and related coefficients at each iteration. The approach is stopped when the maximal number of iterations is reached or when retained coefficients are zeros. Supplementary functions allow to get coefficients of any candidate matrix or mean of coefficients of many candidates. The methods in this package are describing in Girault Gnanguenon Guesse, Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021) "An exploratory penalized regression to identify combined effects of functional variables -Application to agri-environmental issues" <https://hal.archives-ouvertes.fr/hal-03298977>.

This package provides functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) <doi:10.18637/jss.v066.i06>.

Weighted-ensemble regression over base learners supported by caret (Kuhn (2008) <doi:10.18637/jss.v028.i05>), with cross-validated hyperparameter selection, out-of-fold diagnostics, and signed residual- offset estimates. Multi-response problems use iterative input-space expansion related to Spyromitros-Xioufis et al. (2016) <doi:10.1007/s10994-016-5546-z>, with Jacobi or Gauss-Seidel sweeps, package-defined companion gates and per-response iteration stitching. A package-defined pseudo-label stage promotes prediction rows by a cross-model and cross-dataset range ratio and accepts rounds with an out-of-fold squared-correlation gauge.

This package provides a parallel execution runtime for R that emphasizes deterministic memory behavior and efficient handling of large shared inputs. shard enables zero-copy parallel reads via shared/memory-mapped segments, encourages explicit output buffers to avoid large result aggregation, and supervises worker processes to mitigate memory drift via controlled recycling. Diagnostics report peak memory usage, end-of-run memory return, and hidden copy/materialization events to support reproducible performance benchmarking.

This package provides a comparative framework to detect species-specific spatial and thermal responses to climate change using opportunistic occurrence data. Species temporal trends in geographic position (via Earth-Centred Earth-Fixed vector analysis) and environmental variables (temperature and elevation) are contrasted against the overall trend of the complete dataset, allowing classification of species into ecologically interpretable response categories. Approach described in Lobo et al. (2023) <doi:10.1002/ece3.10674>.

This package provides a very bare-bones interface to use the Metropolis-Hastings Monte Carlo Markov Chain algorithm. It is suitable for teaching and testing purposes.

Search for variables across multiple survey datasets, examine variable properties (labels, values, missingness), and explore variable context within datasets. Useful for navigating complex survey data with many variables and understanding variable relationships and metadata.

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 performs survival analysis for one-way layout. The package includes the generalized test for survival ANOVA (Tsui and Weerahandi (1989) <doi:10.2307/2289949> and (Weerahandi, 2004; ISBN:978-0471470175)). It also performs pairwise comparisons and graphical approaches. Moreover, it assesses the weibullness of data in each group via test. The package computes mean and confidence interval under Weibull distribution.