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.

Interoperability between Rcpp and the C++11 array and tuple types. Linking to this package allows fixed-length std::array objects to be converted to and from equivalent R vectors, and std::tuple objects converted to lists, via the as() and wrap() functions. There is also experimental support for std::span from C++20'.

MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, small n large p transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.

We introduce a robust matrix factor model that explicitly incorporates tail behavior and employs a mean-shift term to avoid efficiency losses through pre-centering of observed matrices. More details on the methods related to our paper are currently under submission. A full reference to the paper will be provided in future versions once the paper is published.

Flexible statistical modelling using a modular framework for regression, in which groups of transformations are composed together and act on probability distributions.

Bindings for additional models for use with the parsnip package. Models include prediction rule ensembles (Friedman and Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992 ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013) <doi:10.1007/978-1-4614-6849-3>.

Reference database manager offering a set of functions to import, organize, clean, filter, audit and export reference genetic data. Provide functions to download sequence data from NCBI GenBank <https://www.ncbi.nlm.nih.gov/genbank/>. Designed as an environment for semi-automatic and assisted construction of reference databases and to improve standardization and repeatability in barcoding and metabarcoding studies.

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The rdmulti package provides tools to analyze RD designs with multiple cutoffs or scores: rdmc() estimates pooled and cutoff specific effects for multi-cutoff designs, rdmcplot() draws RD plots for multi-cutoff designs and rdms() estimates effects in cumulative cutoffs or multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2020) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2020_Stata.pdf> for further methodological details.

Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the whole world thanks to the Iowa Environment Mesonet website.

The Brazilian Central Bank API delivers many datasets which regard economic activity, regional economy, international economy, public finances, credit indicators and many more. For more information please see <http://dadosabertos.bcb.gov.br/>. These datasets can be accessed through rbcb functions and can be obtained in different data structures common to R ('tibble', data.frame', xts', ...).

Leaf angle distribution is described by a number of functions (e.g. ellipsoidal, Beta and rotated ellipsoidal). The parameters of leaf angle distributions functions are estimated through different empirical relationship. This package includes estimations of parameters of different leaf angle distribution function, plots and evaluates leaf angle distribution functions, calculates extinction coefficients given leaf angle distribution. Reference: Wang(2007)<doi:10.1016/j.agrformet.2006.12.003>.

Reads data files acquired by Bruker Daltonics matrix-assisted laser desorption/ionization-time-of-flight mass spectrometer of the *flex series.

This package contains several useful navigation helper functions, including easily building folder paths, quick viewing dataframes in Excel', creating date vectors and changing the console prompt to reflect time.

This package provides functions for reading mass spectrometry data in mzXML format.

This package provides a toolkit for analyzing classifier performance by using receiver operating characteristic (ROC) curves. Performance may be assessed on a single classifier or multiple ones simultaneously, making it suitable for comparisons. In addition, different metrics allow the evaluation of local performance when working within restricted ranges of sensitivity and specificity. For details on the different implementations, see McClish D. K. (1989) <doi:10.1177/0272989X8900900307>, Vivo J.-M., Franco M. and Vicari D. (2018) <doi:10.1007/S11634-017-0295-9>, Jiang Y., et al (1996) <doi:10.1148/radiology.201.3.8939225>, Franco M. and Vivo J.-M. (2021) <doi:10.3390/math9212826> and Carrington, André M., et al (2020) <doi: 10.1186/s12911-019-1014-6>.

This package provides tools for downloading and analyzing CDC NHANES data, with a focus on analytical laboratory data.

Regularised discriminant analysis functions. The classical regularised discriminant analysis proposed by Friedman in 1989, including cross-validation, of which the linear and quadratic discriminant analyses are special cases. Further, the regularised maximum likelihood linear discriminant analysis, including cross-validation. References: Friedman J.H. (1989): "Regularized Discriminant Analysis". Journal of the American Statistical Association 84(405): 165--175. <doi:10.2307/2289860>. Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>. Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>.

Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of the least trimmed square loss of the models in the ensembles under constraints for the size of the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and diversity of the robust ensemble are selected by cross-validation.

Automatic coding of open-ended responses to the Cognitive Reflection Test (CRT), a widely used class of tests in cognitive science and psychology that assess the tendency to override an initial intuitive (but incorrect) answer and engage in reflection to reach a correct solution. The package standardizes CRT response coding across datasets in cognitive psychology, decision-making, and related fields. Automated coding reduces manual effort and improves reproducibility by limiting variability from subjective interpretation of open-ended responses. The package supports automatic coding and machine scoring for the original English-language CRT (Frederick, 2005) <doi:10.1257/089533005775196732>, CRT4 and CRT7 (Toplak et al., 2014) <doi:10.1080/13546783.2013.844729>, CRT-long (Primi et al., 2016) <doi:10.1002/bdm.1883>, and CRT-2 (Thomson & Oppenheimer, 2016) <doi:10.1017/s1930297500007622>.

Extends R Commander with a unified menu of new and pre-existing statistical functions related to public management and policy analysis statistics. Functions and menus have been renamed according to the usage in PMGT 630 in the Master of Public Administration program at Brigham Young University.

HTML formats and templates for rmarkdown documents, with some extra features such as automatic table of contents, lightboxed figures, dynamic crosstab helper.

Data and Functions from the book R Graphics, Third Edition. There is a function to produce each figure in the book, plus several functions, classes, and methods defined in Chapter 8.

Biologically relevant, yet mathematically sound constraints are used to compute the propensity and thence infer the dominant direction of reactions of a generic biochemical network. The reactions must be unique and their number must exceed that of the reactants,i.e., reactions >= reactants + 2. ReDirection', computes the null space of a user-defined stoichiometry matrix. The spanning non-zero and unique reaction vectors (RVs) are combinatorially summed to generate one or more subspaces recursively. Every reaction is represented as a sequence of identical components across all RVs of a particular subspace. The terms are evaluated with (biologically relevant bounds, linear maps, tests of convergence, descriptive statistics, vector norms) and the terms are classified into forward-, reverse- and equivalent-subsets. Since, these are mutually exclusive the probability of occurrence is binary (all, 1; none, 0). The combined propensity of a reaction is the p1-norm of the sub-propensities, i.e., sum of the products of the probability and maximum numeric value of a subset (least upper bound, greatest lower bound). This, if strictly positive is the probable rate constant, is used to infer dominant direction and annotate a reaction as "Forward (f)", "Reverse (b)" or "Equivalent (e)". The inherent computational complexity (NP-hard) per iteration suggests that a suitable value for the number of reactions is around 20. Three functions comprise ReDirection. These are check_matrix() and reaction_vector() which are internal, and calculate_reaction_vector() which is external.

Integrates the Groovy scripting language with the R Project for Statistical Computing.