The Discrete Transmuted Generalized Inverse Weibull (DTGIW) distribution is a new distribution for count data analysis. The DTGIW is discrete distribution based on Atchanut and Sirinapa (2021). <DOI: 10.14456/sjst-psu.2021.149>.

This package provides tools for exploring the topography of 3d triangle meshes. The functions were developed with dental surfaces in mind, but could be applied to any triangle mesh of class mesh3d'. More specifically, doolkit allows to isolate the border of a mesh, or a subpart of the mesh using the polygon networks method; crop a mesh; compute basic descriptors (elevation, orientation, footprint area); compute slope, angularity and relief index (Ungar and Williamson (2000) <https://palaeo-electronica.org/2000_1/gorilla/issue1_00.htm>; Boyer (2008) <doi:10.1016/j.jhevol.2008.08.002>), inclination and occlusal relief index or gamma (Guy et al. (2013) <doi:10.1371/journal.pone.0066142>), OPC (Evans et al. (2007) <doi:10.1038/nature05433>), OPCR (Wilson et al. (2012) <doi:10.1038/nature10880>), DNE (Bunn et al. (2011) <doi:10.1002/ajpa.21489>; Pampush et al. (2016) <doi:10.1007/s10914-016-9326-0>), form factor (Horton (1932) <doi:10.1029/TR013i001p00350>), basin elongation (Schum (1956) <doi:10.1130/0016-7606(1956)67[597:EODSAS]2.0.CO;2>), lemniscate ratio (Chorley et al; (1957) <doi:10.2475/ajs.255.2.138>), enamel-dentine distance (Guy et al. (2015) <doi:10.1371/journal.pone.0138802>; Thiery et al. (2017) <doi:10.3389/fphys.2017.00524>), absolute crown strength (Schwartz et al. (2020) <doi:10.1098/rsbl.2019.0671>), relief rate (Thiery et al. (2019) <doi:10.1002/ajpa.23916>) and area-relative curvature; draw cumulative profiles of a topographic variable; and map a variable over a 3d triangle mesh.

Output graphics to EMF+/EMF.

We provide three distance metrics for measuring the separation between two clusters in high-dimensional spaces. The first metric is the centroid distance, which calculates the Euclidean distance between the centers of the two groups. The second is a ridge Mahalanobis distance, which incorporates a ridge correction constant, alpha, to ensure that the covariance matrix is invertible. The third metric is the maximal data piling distance, which computes the orthogonal distance between the affine spaces spanned by each class. These three distances are asymptotically interconnected and are applicable in tasks such as discrimination, clustering, and outlier detection in high-dimensional settings.

Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.

There are many different formats dates are commonly represented with: the order of day, month, or year can differ, different separators ("-", "/", or whitespace) can be used, months can be numerical, names, or abbreviations and year given as two digits or four. datefixR takes dates in all these different formats and converts them to R's built-in date class. If datefixR cannot standardize a date, such as because it is too malformed, then the user is told which date cannot be standardized and the corresponding ID for the row. datefixR also allows the imputation of missing days and months with user-controlled behavior.

Several tools for handling block-matrix diagonals and similar constructs are implemented. Block-diagonal matrices can be extracted or removed using two small functions implemented here. In addition, non-square matrices are supported. Block diagonal matrices occur when two dimensions of a data set are combined along one edge of a matrix. For example, trade-flow data in the decompr and gvc packages have each country-industry combination occur along both edges of the matrix.

Create quick and easy dot-and-whisker plots of regression results. It takes as input either (1) a coefficient table in standard form or (2) one (or a list of) fitted model objects (of any type that has methods implemented in the parameters package). It returns ggplot objects that can be further customized using tools from the ggplot2 package. The package also includes helper functions for tasks such as rescaling coefficients or relabeling predictor variables. See more methodological discussion of the visualization and data management methods used in this package in Kastellec and Leoni (2007) <doi:10.1017/S1537592707072209> and Gelman (2008) <doi:10.1002/sim.3107>.

It is used to identify dysregulated pathways based on a pre-ranked gene pair list. A fast algorithm is used to make the computation really fast. The data in package DysPIAData is needed.

Enables the user to build a citation network/graph from bibliographic data and, based on modularity and heterocitation metrics, assess the degree of awareness/cross-fertilization between two corpora/communities. This toolset is optimized for Scopus data.

Dual Scaling, developed by Professor Shizuhiko Nishisato (1994, ISBN: 0-9691785-3-6), is a fundamental technique in multivariate analysis used for data scaling and correspondence analysis. Its utility lies in its ability to represent multidimensional data in a lower-dimensional space, making it easier to visualize and understand underlying patterns in complex data. This technique has been implemented to handle various types of data, including Contingency and Frequency data (CF), Multiple-Choice data (MC), Sorting data (SO), Paired-Comparison data (PC), and Rank-Order data (RO), providing users with a powerful tool to explore relationships between variables and observations in various fields, from sociology to ecology, enabling deeper and more efficient analysis of multivariate datasets.

This package provides tools for converting and imputing date values to the ISO 8601 standard format and for reconciling differences between two versions of a data set. The package automatically detects date patterns within data frame columns and converts them to consistent ISO-formatted dates, with optional imputation of missing day or month components based on user-defined rules. It also includes functionality to identify inserted, deleted, and updated records, as well as column- and value-level changes, when comparing old and new versions of a data frame. Only one date format may be applied within a single column.

Simulation tool to estimate the rate of success that surveys possessing user-specific characteristics have in identifying archaeological sites (or any groups of clouds of objects), given specific parameters of survey area, survey methods, and site properties. The survey approach used is largely based on the work of Kintigh (1988) <doi:10.2307/281113>.

This package provides a set of pricing and expository functions that should be useful in teaching a course on financial derivatives.

Simple Principal Components Analysis (PCA) and (Multiple) Correspondence Analysis (CA) based on the Singular Value Decomposition (SVD). This package provides S4 classes and methods to compute, extract, summarize and visualize results of multivariate data analysis. It also includes methods for partial bootstrap validation described in Greenacre (1984, ISBN: 978-0-12-299050-2) and Lebart et al. (2006, ISBN: 978-2-10-049616-7).

This package provides methods for (auto)covariance/correlation function estimation in change point regression with stationary errors circumventing the pre-estimation of the underlying signal of the observations. Generic, first-order, (m+1)-gapped, difference-based autocovariance function estimator is based on M. Levine and I. Tecuapetla-Gómez (2023) <doi:10.48550/arXiv.1905.04578>. Bias-reducing, second-order, (m+1)-gapped, difference-based estimator is based on I. Tecuapetla-Gómez and A. Munk (2017) <doi:10.1111/sjos.12256>. Robust autocovariance estimator for change point regression with autoregressive errors is based on S. Chakar et al. (2017) <doi:10.3150/15-BEJ782>. It also includes a general projection-based method for covariance matrix estimation.

Implementing Function-on-Scalar Regression model in which the response function is dichotomized and observed sparsely. This package provides smooth estimations of functional regression coefficients and principal components for the dichotomized functional response regression (dfrr) model.

Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package dynr (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state-space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single-subject time series data or multiple-subject longitudinal data. Ou, Hunter, & Chow (2019) <doi:10.32614%2FRJ-2019-012> provided a detailed introduction to the interface and more information on the algorithms.

This package provides the ability to display something analogous to Python's docstrings within R. By allowing the user to document their functions as comments at the beginning of their function without requiring putting the function into a package we allow more users to easily provide documentation for their functions. The documentation can be viewed just like any other help files for functions provided by packages as well.

Create and manage fault-tolerant task queues for the foreach package using the Redis key/value database.

Makes deck.gl <https://deck.gl/>, a WebGL-powered open-source JavaScript framework for visual exploratory data analysis of large datasets, available within R via the htmlwidgets package. Furthermore, it supports basemaps from mapbox <https://www.mapbox.com/> via mapbox-gl-js <https://github.com/mapbox/mapbox-gl-js>.

Offers functionality which provides methods for data analyses and cleaning that can be flexibly applied across multiple variables and in groups. These include cleaning accidental text, contingent calculations, counting missing data, and building summarizations of the data.

DataSHIELD is an infrastructure and series of R packages that enables the remote and non-disclosive analysis of sensitive research data. This package is the DataSHIELD interface implementation to analyze data shared on a MOLGENIS Armadillo server. MOLGENIS Armadillo is a light-weight DataSHIELD server using a file store and an RServe server.

Perform tree-ring analyses such as detrending, chronology building, and cross dating. Read and write standard file formats used in dendrochronology.