An interface to the BaM (Bayesian Modeling) engine, a Fortran'-based executable aimed at estimating a model with a Bayesian approach and using it for prediction, with a particular focus on uncertainty quantification. Classes are defined for the various building blocks of BaM inference (model, data, error models, Markov Chain Monte Carlo (MCMC) samplers, predictions). The typical usage is as follows: (1) specify the model to be estimated; (2) specify the inference setting (dataset, parameters, error models...); (3) perform Bayesian-MCMC inference; (4) read, analyse and use MCMC samples; (5) perform prediction experiments. Technical details are available (in French) in Renard (2017) <https://hal.science/hal-02606929v1>. Examples of applications include Mansanarez et al. (2019) <doi:10.1029/2018WR023389>, Le Coz et al. (2021) <doi:10.1002/hyp.14169>, Perret et al. (2021) <doi:10.1029/2020WR027745>, Darienzo et al. (2021) <doi:10.1029/2020WR028607> and Perret et al. (2023) <doi:10.1061/JHEND8.HYENG-13101>.

Enhances the R Optimization Infrastructure ('ROI') package by registering the ipop solver from package kernlab'.

An implementation of the QUEFTS (Quantitative Evaluation of the Native Fertility of Tropical Soils) model. The model (1) estimates native nutrient (N, P, K) supply of soils from a few soil chemical properties; and (2) computes crop yield given that supply, crop parameters, fertilizer application, and crop attainable yield. See Janssen et al. (1990) <doi:10.1016/0016-7061(90)90021-Z> for the technical details and Sattari et al. (2014) <doi:10.1016/j.fcr.2013.12.005> for a recent evaluation and improvements.

This package provides methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.

The RCC_PCA criterion is a tool to determine the optimal number of components to retain in PCA;See Alshammri (2021).

Export all data, including metadata, from a REDCap (Research Electronic Data Capture) Project via the REDCap API <https://projectredcap.org/wp-content/resources/REDCapTechnicalOverview.pdf>. The exported (meta)data will be processed and formatted into a stand alone R data package which can be installed and shared between researchers. Several default reports are generated as vignettes in the resulting package.

Offers a handful of useful wrapper functions which streamline the reading, analyzing, and visualizing of variant call format (vcf) files in R. This package was designed to facilitate an explicit pipeline for optimizing Stacks (Rochette et al., 2019) (<doi:10.1111/mec.15253>) parameters during de novo (without a reference genome) assembly and variant calling of restriction-enzyme associated DNA sequence (RADseq) data. The pipeline implemented here is based on the 2017 paper "Lost in Parameter Space" (Paris et al., 2017) (<doi:10.1111/2041-210X.12775>) which establishes clear recommendations for optimizing the parameters m', M', and n', during the process of assembling loci.

Compute the values of various parameters evaluating how similar two multidimensional datasets structures are in multidimensional space, as described in: Jouan-Rimbaud, D., Massart, D. L., Saby, C. A., Puel, C. (1998), <doi:10.1016/S0169-7439(98)00005-7>. The computed parameters evaluate three properties, namely, the direction of the data sets, the variance-covariance of the data points, and the location of the data sets centroids. The package contains workhorse function jrparams(), as well as two helper functions Mboxtest() and JRsMahaldist(), and four example data sets.

This package provides a collection of functions to compute the Rao-Stirling diversity index (Porter and Rafols, 2009) <DOI:10.1007/s11192-008-2197-2> and its extension to acknowledge missing data (i.e., uncategorized references) by calculating its interval of uncertainty using mathematical optimization as proposed in Calatrava et al. (2016) <DOI:10.1007/s11192-016-1842-4>. The Rao-Stirling diversity index is a well-established bibliometric indicator to measure the interdisciplinarity of scientific publications. Apart from the obligatory dataset of publications with their respective references and a taxonomy of disciplines that categorizes references as well as a measure of similarity between the disciplines, the Rao-Stirling diversity index requires a complete categorization of all references of a publication into disciplines. Thus, it fails for a incomplete categorization; in this case, the robust extension has to be used, which encodes the uncertainty caused by missing bibliographic data as an uncertainty interval. Classification / ACM - 2012: Information systems ~ Similarity measures, Theory of computation ~ Quadratic programming, Applied computing ~ Digital libraries and archives.

This package provides a useful statistical tool for the construction and analysis of Honeycomb Selection Designs. More information about this type of designs: Fasoula V. (2013) <doi:10.1002/9781118497869.ch6> Fasoula V.A., and Tokatlidis I.S. (2012) <doi:10.1007/s13593-011-0034-0> Fasoulas A.C., and Fasoula V.A. (1995) <doi:10.1002/9780470650059.ch3> Tokatlidis I. (2016) <doi:10.1017/S0014479715000150> Tokatlidis I., and Vlachostergios D. (2016) <doi:10.3390/d8040029>.

This package provides functions to safely map from a vector of keys to a vector of values, determine properties of a given relation, or ensure a relation conforms to a given type, such as many-to-many, one-to-many, injective, surjective, or bijective. Permits default return values for use similar to a vectorised switch statement, as well as safely handling large vectors, NAs, and duplicate mappings.

MsgPack header files are provided for use by R packages, along with the ability to access, create and alter MsgPack objects directly from R. MsgPack is an efficient binary serialization format. It lets you exchange data among multiple languages like JSON but it is faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves. This package provides headers from the msgpack-c implementation for C and C++(11) for use by R, particularly Rcpp'. The included msgpack-c headers are licensed under the Boost Software License (Version 1.0); the code added by this package as well the R integration are licensed under the GPL (>= 2). See the files COPYRIGHTS and AUTHORS for a full list of copyright holders and contributors to msgpack-c'.

Dynamic Programming implemented in Rcpp'. Includes example partition and out of sample fitting applications. Also supplies additional custom coders for the vtreat package.

Play the classic game of tic-tac-toe (naughts and crosses).

An expansion of R's stats random wishart matrix generation. This package allows the user to generate singular, Uhlig and Harald (1994) <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate wishart matrices with fractional degrees of freedom, Adhikari (2008) <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility modeling. Users can also use this package to create random covariance matrices.

This package provides a carbon-water coupled model (TRIPLEX-CW-Flux) is based on two well-established models, TRIPLEX-Flux model and Penmanâ Monteith model, integrates soil water and water vapor pressure deficits into the stomata conductance submodule to estimate net ecosystem production and evapotranspiration in forest ecosystems.<https://github.com/ShulanSun/rTRIPLEX_CW_Flux>.

This package provides bioaccumulation factors from a toxicokinetic model fitted to accumulation-depuration data. It is designed to fulfil the requirements of regulators when examining applications for market authorization of active substances.

This package provides a single key function, Require that makes rerun-tolerant versions of install.packages and `require` for CRAN packages, packages no longer on CRAN (i.e., archived), specific versions of packages, and GitHub packages. This approach is developed to create reproducible workflows that are flexible and fast enough to use while in development stages, while able to build snapshots once a stable package collection is found. As with other functions in a reproducible workflow, this package emphasizes functions that return the same result whether it is the first or subsequent times running the function, with subsequent times being sufficiently fast that they can be run every time without undue waiting burden on the user or developer.

Computes the ridge partial correlation coefficients in a high or ultra-high dimensional linear regression problem. An extended Bayesian information criterion is also implemented for variable selection. Users provide the matrix of covariates as a usual dense matrix or a sparse matrix stored in a compressed sparse column format. Detail of the method is given in the manual.

Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.This package implements the methods described by Hu et al. (2022) <doi:10.1002/sim.9548>.

This package provides a method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) <doi:10.1016/j.jspi.2007.11.003>.

Implementing the BDAT tree taper Fortran routines, which were developed for the German National Forest Inventory (NFI), to calculate diameters, volume, assortments, double bark thickness and biomass for different tree species based on tree characteristics and sorting information. See Kublin (2003) <doi:10.1046/j.1439-0337.2003.00183.x> for details.

Adds subtotal rows / sections (a la the SAS Proc Tabulate All option) to a Group By output by running a series of Group By functions with partial sets of the same variables and combining the results with the original. Can be used to add comprehensive information to a data report or to quickly aggregate Group By outputs used to gain a greater understanding of data.

OpenRefine (formerly Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and OpenRefine'. Available functionality includes project import, export and deletion.