This package provides tools for quantitative plant disease epidemiology, including functional analysis of disease progress curves, spatial epidemiology, disease quantification, and weather-driven epidemic analysis. The package also serves as an educational resource and companion to the book "R for Plant Disease Epidemiology" (R4PDE). Several functions are based on classical and contemporary methods, including those discussed in Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>.

This package performs univariate probability mass function estimation via Bayesian nonparametric mixtures of rounded kernels as in Canale and Dunson (2011) <doi:10.1198/jasa.2011.tm10552>.

Download and parse public files released by B3 and convert them into useful formats and data structures common to data analysis practitioners.

Facilitates the use of machine learning algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.5.0 improved mparheuristic function (new hyperparameter heuristics); 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 - improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.

This is a port of Jonathan Shewchuk's Triangle library to R. From his description: "Triangle generates exact Delaunay triangulations, constrained Delaunay triangulations, conforming Delaunay triangulations, Voronoi diagrams, and high-quality triangular meshes. The latter can be generated with no small or large angles, and are thus suitable for finite element analysis.".

Helps users in quickly visualizing risk-of-bias assessments performed as part of a systematic review. It allows users to create weighted bar-plots of the distribution of risk-of-bias judgments within each bias domain, in addition to traffic-light plots of the specific domain-level judgments for each study. The resulting figures are of publication quality and are formatted according the risk-of-bias assessment tool use to perform the assessments. Currently, the supported tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019) <doi:10.1136/bmj.l4898>), ROBINS-I (for non-randomised studies of interventions; Sterne et al (2016) <doi:10.1136/bmj.i4919>), and QUADAS-2 (for diagnostic accuracy studies; Whiting et al (2011) <doi:10.7326/0003-4819-155-8-201110180-00009>).

This package provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis. A good reference on these methods is Chapter 10 of Wu, C-F J and Hamada, M (2009) "Experiments: Planning, Analysis, and Parameter Design Optimization" ISBN 978-0-471-69946-0. An early version of the package is documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.

Iterative least cost path and minimum spanning tree methods for projecting forest road networks. The methods connect a set of target points to an existing road network using igraph <https://igraph.org> to identify least cost routes. The cost of constructing a road segment between adjacent pixels is determined by a user supplied weight raster and a weight function; options include the average of adjacent weight raster values, and a function of the elevation differences between adjacent cells that penalizes steep grades. These road network projection methods are intended for integration into R workflows and modelling frameworks used for forecasting forest change, and can be applied over multiple time-steps without rebuilding a graph at each time-step.

The ropenblas package (<https://prdm0.github.io/ropenblas/>) is useful for users of any GNU/Linux distribution. It will be possible to download, compile and link the OpenBLAS library (<https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the GNU/Linux distribution used. With the ropenblas package it is possible to download, compile and link the latest version of the OpenBLAS library even the repositories of the GNU/Linux distribution used do not include the latest versions of OpenBLAS'. If of interest, older versions of the OpenBLAS library may be considered. Linking R with an optimized version of BLAS (<https://netlib.org/blas/>) may improve the computational performance of R code. The OpenBLAS library is an optimized implementation of BLAS that can be easily linked to R with the ropenblas package.

This package provides various statistical methods for designing and analyzing two-stage randomized controlled trials using the methods developed by Imai, Jiang, and Malani (2021) <doi:10.1080/01621459.2020.1775612> and (2022+) <doi:10.48550/arXiv.2011.07677>. The package enables the estimation of direct and spillover effects, conduct hypotheses tests, and conduct sample size calculation for two-stage randomized controlled trials.

Examples for Seamless R and C++ integration The Rcpp package contains a C++ library that facilitates the integration of R and C++ in various ways. This package provides some usage examples. Note that the documentation in this package currently does not cover all the features in the package. The site <https://gallery.rcpp.org> regroups a large number of examples for Rcpp'.

This package provides a collection of functions to simulate dice rolls and the like. In particular, experiments and exercises can be performed looking at combinations and permutations of values in dice rolls and coin flips, together with the corresponding frequencies of occurrences. When applying each function, the user has to input the number of times (rolls, flips) to toss the dice. Needless to say, the more the tosses, the more the frequencies approximate the actual probabilities. Moreover, the package provides functions to generate non-transitive sets of dice (like Efron's) and to check whether a given set of dice is non-transitive with given probability.

This package provides the log-likelihoods with gradients from stan (Carpenter et al (2015), <doi:10.48550/arXiv.1509.07164>) needed for generalized log-likelihood estimation in nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is split of to reduce computational burden of recompiling rxode2 (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs the nlmixr2 models during estimation.

Access the Refuge API, a web-application for locating trans and intersex-friendly restrooms, including unisex and accessible restrooms. Includes data on the location of restrooms, along with directions, comments, user ratings and amenities. Coverage is global, but data is most comprehensive in the United States. See <https://www.refugerestrooms.org/api/docs/> for full API documentation.

This is a wrapper function for image(), which makes reasonable raster plots with nice axis and other useful features.

Various tools for handling fuzzy measures, calculating Shapley value and interaction index, Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using lpsolve package, whose source in C adapted to the R environment is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package. Please refer to the following: [1] <https://personal-sites.deakin.edu.au/~gleb/fmtools.html> [2] G. Beliakov, H. Bustince, T. Calvo, A Practical Guide to Averaging', Springer, (2016, ISBN: 978-3-319-24753-3). [3] G. Beliakov, S. James, J-Z. Wu, Discrete Fuzzy Measures', Springer, (2020, ISBN: 978-3-030-15305-2).

The receiver operating characteristic (ROC) curve is one of the most widely used tools for evaluating diagnostic and prognostic biomarkers across diverse scientific fields, particularly in medicine. Despite its ubiquity, ROC estimation and testing methods differ substantially in their assumptions and resulting curve properties. This package provides a unified framework for constructing, visualizing, and comparing parametric, nonparametric, semiparametric, and Bayesian ROC curves. ROCModels helps researchers identify and implement ROC inference methods most suitable for their data. See the accompanying vignette ROCModels_Package_Doc for a detailed introduction. Alonzo, T. A., and Pepe, M. S. (2002) <doi: 10.1093/biostatistics/3.3.421>, Andrews, D. F., and Herzberg, A. M. (1985) <doi: 10.1007/978-1-4612-5098-2>, Bamber, D. (1975) <doi: 10.1016/0022-2496(75)90001-2>, Cox, D. R. (1972) <doi:10.1111/j.2517-6161.1972.tb00899.x>, Cox, D. R. (1975) <doi: 10.1093/biomet/62.2.269>, DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988) <doi: 10.2307/2531595>, Dorfman, D. D., and Alf, E. (1969) <doi: 10.1016/0022-2496(69)90019-4>, Dorfman, D. D., Berbaum, K. S., and Metz, C. E. (1997) <doi: 10.1016/s1076-6332(97)80013-x>, Erkanli, A., Sung, L., and Stamey, J. D. (2006) <doi: 10.1002/sim.2496>, Faraggi, D., and Reiser, B. (2002) <doi: 10.1002/sim.1228>, Ghebremichael, M., and Habtemicael, S. (2018) <doi: 10.1080/02664763.2017.1420758>, Ghebremichael, M., and Michael, H. (2024) <doi: 10.1080/03610918.2022.2032159>, Ghebremichael, M., Michael, H., Tubbs, J., and Paintsil, E. (2019) <doi: 10.3844/jmssp.2019.55.64>, Gönen, M., and Heller, G. (2010) <doi: 10.1177/0272989X09360067>, Gopalakrishnan, V., Bose, E., Nair, U., Cheng, Y., and Ghebremichael, M. (2020) <doi: 10.1186/s12879-020-05458-w>, Green, D. M., and Swets, J. A. (1966, ISBN:0471324205), Gu, J., and Ghosal, S. (2009) <doi: 10.1016/j.jspi.2008.09.014>, Gu, Y., Ghosal, S., and Roy, A. (2008) <doi: 10.1002/sim.3366>, Guidoum, A. C. (2020) <doi: 10.32614/CRAN.package.kedd>, <doi: 10.48550/arXiv.2012.06102>, Guo, B. (2015) <https://d-scholarship.pitt.edu/23590/1/Guo_Ben_thesis_12-2014.pdf>, Hanley, J. A., and McNeil, B. J. (1982) <doi: 10.1148/radiology.143.1.7063747>, Hsieh, F., and Turnbull, B. W. (1996) <doi: 10.1214/aos/1033066197>, Hussain, E. (2012) <doi: 10.6000/1927-5129.2012.08.02.09>, Ishwaran, H., and James, L. F. (2002) <doi: 10.1198/106186002411>, Jokiel-Rokita, A., and Topolnicki, R. (2020) <doi: 10.1016/j.csda.2019.106820>, Krzanowski, W. J., and Hand, D. J. (2009) <doi: 10.1201/9781439800225>, Kundu, D., and Gupta, R. D. (2006) <doi: 10.1109/TR.2006.874918>, Lloyd, C. J. (1998) <doi: 10.1080/01621459.1998.10473797>, Lehmann, E. L. (1953) <doi: 10.1214/aoms/1177729080>, Metz, C. E., Herman, B. A., and Shen, J. H. (1998) <doi:10.1002/(SICI)1097-0258(19980515)17:9%3C1033::AID-SIM784%3E3.0.CO;2-Z>, Pepe, M. S. (2003) <doi: 10.1093/oso/9780198509844.001.0001>, Pundir, S., and Amala, R. (2014) <doi: 10.22237/jmasm/1398917940>, Silverman, B. W. (2018) <doi: 10.1201/9781315140919>, Yeo, I. K., and Johnson, R. A. (2000) <doi: 10.1093/biomet/87.4.954>, Zhou, X. H., McClish, D. K., and Obuchowski, N. A. (2009) <doi: 10.1002/9780470906514>, Zou, K. H., Hall, W. J., and Shapiro, D. E. (1997) <doi: 10.1002/(SICI)1097-0258(19971015)16:19%3C2143::AID-SIM655%3E3.0.CO;2-3>.

This package contains implementations of recurrent event data analysis routines including (1) survival and recurrent event data simulation from stochastic process point of view by the thinning method proposed by Lewis and Shedler (1979) <doi:10.1002/nav.3800260304> and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976), (2) the mean cumulative function (MCF) estimation by the Nelson-Aalen estimator of the cumulative hazard rate function, (3) two-sample recurrent event responses comparison with the pseudo-score tests proposed by Lawless and Nadeau (1995) <doi:10.2307/1269617>, (4) gamma frailty model with spline rate function following Fu, et al. (2016) <doi:10.1080/10543406.2014.992524>.

This package creates the radar-boxplot, a plot that was created by the author during his Ph.D. in forest resources. The radar-boxplot is a visualization feature suited for multivariate classification/clustering. It provides an intuitive deep understanding of the data.

This package provides an R scripting interface to the open-source SAGA-GIS (System for Automated Geoscientific Analyses Geographical Information System) software. Rsagacmd dynamically generates R functions for every SAGA-GIS geoprocessing tool based on the user's currently installed SAGA-GIS version. These functions are contained within an S3 object and are accessed as a named list of libraries and tools. This structure facilitates an easier scripting experience by organizing the large number of SAGA-GIS geoprocessing tools (>700) by their respective library. Interactive scripting can fully take advantage of code autocompletion tools (e.g. in RStudio'), allowing for each tools syntax to be quickly recognized. Furthermore, the most common types of spatial data (via the terra', sp', and sf packages) along with non-spatial data are automatically passed from R to the SAGA-GIS command line tool for geoprocessing operations, and the results are loaded as the appropriate R object. Outputs from individual SAGA-GIS tools can also be chained using pipes from the magrittr and dplyr packages to combine complex geoprocessing operations together in a single statement. SAGA-GIS is available under a GPLv2 / LGPLv2 licence from <https://sourceforge.net/projects/saga-gis/> including Windows x86/x64 and macOS binaries. SAGA-GIS is also included in Debian/Ubuntu default software repositories. Rsagacmd has currently been tested on SAGA-GIS versions from 2.3.1 to 9.5.1 on Windows, Linux and macOS.

Robust tests (RW, RPB and RGF) are provided for testing the equality of several long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW, RPB and RGF tests are robust versions of Welch's F test proposed by Welch (1951) <doi:10.2307/2332579>, parametric bootstrap test proposed by Krishnamoorthy et. al (2007) <doi:10.1016/j.csda.2006.09.039>; and generalized F test proposed by Weerahandi (1995) <doi:10.2307/2532947>;, respectively. These tests are based on the modified maximum likelihood (MML) estimators proposed by Tiku(1967, 1968) <doi:10.2307/2333859>, <doi:10.1080/01621459.1968.11009228>.

Interface to access data via the United States Department of Agriculture's National Agricultural Statistical Service (NASS) Quick Stats web API <https://quickstats.nass.usda.gov/api/>. Convenience functions facilitate building queries based on available parameters and valid parameter values. This product uses the NASS API but is not endorsed or certified by NASS.

Selected functions for simulation and regression of integrated Isothermal Titration Calorimetry (ITC) data with the most commonly used one-to-one binding model.

Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 <doi:10.1214/12-BA719>).