This package provides a new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. It is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. The matrix is divided into the part corresponding to the relative rotation and the bias of the empirical orthogonal functions of the data. The full set of diagnostics produced by the analysis of the errors between model and observational vector datasets comprises the errors in the means, the analysis of the total variance of both datasets, the rotation matrix corresponding to the principal components in observation and model, the angle of rotation of model-derived empirical orthogonal functions respect to the ones from observations, the standard deviation of model and observations, the root mean squared error between both datasets and the squared two-dimensional correlation coefficient. See the output of function UVError() in this package.

Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE, heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.

This package provides a set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images.

Explore continuous, date and categorical variables. sumvar aims to bring the ease and simplicity of the "sum" and "tab" functions from stata'.

An Electronic Data Capture system (EDC) and Data Standard agnostic solution that enables the pharmaceutical programming community to develop Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) datasets in R. The reusable algorithms concept in sdtm.oak provides a framework for modular programming and can potentially automate the conversion of raw clinical data to SDTM through standardized SDTM specifications. SDTM is one of the required standards for data submission to the Food and Drug Administration (FDA) in the United States and Pharmaceuticals and Medical Devices Agency (PMDA) in Japan. SDTM standards are implemented following the SDTM Implementation Guide as defined by CDISC <https://www.cdisc.org/standards/foundational/sdtmig>.

Allows the user to connect with the World Spider Catalogue (WSC; <https://wsc.nmbe.ch/>) and the World Spider Trait (WST; <https://spidertraits.sci.muni.cz/>) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/>), and mapping.

This package provides a collection of functions for estimating spatial and spatio-temporal regression models. Moran eigenvectors are used as spatial basis functions to efficiently approximate spatially dependent Gaussian processes (i.e., random effects eigenvector spatial filtering; see Murakami and Griffith 2015 <doi: 10.1007/s10109-015-0213-7>). The implemented models include linear regression with residual spatial dependence, spatially/spatio-temporally varying coefficient models (Murakami et al., 2017, 2024; <doi:10.1016/j.spasta.2016.12.001>,<doi:10.48550/arXiv.2410.07229>), spatially filtered unconditional quantile regression (Murakami and Seya, 2019 <doi:10.1002/env.2556>), Gaussian and non-Gaussian spatial mixed models through compositionally-warping (Murakami et al. 2021, <doi:10.1016/j.spasta.2021.100520>).

Set of tools aimed at wrapping some of the functionalities of the packages tools, utils and codetools into a nicer format so that an IDE can use them.

The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation.

Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2019) <doi:10.1111/2041-210X.13215>). It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks In addition, the randomizations function provides data-stream randomization methods suitable for GPS data.

This package provides a set of functions to build a scoring model from beginning to end, leading the user to follow an efficient and organized development process, reducing significantly the time spent on data exploration, variable selection, feature engineering, binning and model selection among other recurrent tasks. The package also incorporates monotonic and customized binning, scaling capabilities that transforms logistic coefficients into points for a better business understanding and calculates and visualizes classic performance metrics of a classification model.

This package provides an implementation of simplicial complexes for Topological Data Analysis (TDA). The package includes functions to compute faces, boundary operators, Betti numbers, Euler characteristic, and to construct simplicial complexes. It also implements persistent homology, from building filtrations to computing persistence diagrams, with the aim of helping readers understand the core concepts of computational topology. Methods are based on standard references in persistent homology such as Zomorodian and Carlsson (2005) <doi:10.1007/s00454-004-1146-y> and Chazal and Michel (2021) <doi:10.3389/frai.2021.667963>.

Calculate the statistical power to detect clusters using kernel-based spatial relative risk functions that are estimated using the sparr package. Details about the sparr package methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.

We visualize the standard deviation of a data set as the size of a prism whose volume equals the total volume of several prisms made from the Empirical Cumulative Distribution Function.

Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) <doi:10.1007/BF01068419>, Mielke et al. (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022) <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015) <doi:10.1007/978-3-319-22005-5>. Comprehensive documentation and vignettes guide users through implementation and interpretation of results.

Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported.

This package provides two main functionalities. 1 - Given a system of simultaneous equation, it decomposes the matrix of coefficients weighting the endogenous variables into three submatrices: one includes the subset of coefficients that have a causal nature in the model, two include the subset of coefficients that have a interdependent nature in the model, either at systematic level or induced by the correlation between error terms. 2 - Given a decomposed model, it tests for the significance of the interdependent relationships acting in the system, via Maximum likelihood and Wald test, which can be built starting from the function output. For theoretical reference see Faliva (1992) <doi:10.1007/BF02589085> and Faliva and Zoia (1994) <doi:10.1007/BF02589041>.

The Patient Rule Induction Method (PRIM) is typically used for "bump hunting" data mining to identify regions with abnormally high concentrations of data with large or small values. This package extends this methodology so that it can be applied to binary classification problems and used for prediction.

Testing of soil for the contents of organic carbon, and available macro- and micro-nutrients is a crucial part of soil fertility assessment. This package computes some routinely tested soil properties viz. organic carbon (C), total nitrogen (N), available N, mineral N, available phosphorus (P), available potassium (K), available iron (Fe), available zinc (Zn), available manganese (Mn), available copper (Cu), and available nickel (Ni) in soil based on laboratory analysis data obtained by most commonly followed protocols. Besides, it can also draw standard curves based on absorption/emission vs. concentration data, and give out unknown concentrations from absorption/emission readings.

An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) <doi:10.1016/j.isci.2020.100882> for more details.

Collection (syllogi in greek) of real and fictitious data sets for teaching purposes. The datasets were manually entered by the author from the respective references as listed in the individual dataset documentation. The fictions datasets are the creation of the author, that he has found useful for teaching statistics.

This package provides functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables.

Data sets and sample lmer analyses corresponding to the examples in Littell, Milliken, Stroup and Wolfinger (1996), "SAS System for Mixed Models", SAS Institute.

Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class spectra that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra.