This package provides tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.3150/23-BEJ1711>.

This package provides a flexible framework for fitting multivariate ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.

Density, distribution function, quantile function, and random generation function based on Salem, H. M. (2019)<doi:10.5539/mas.v13n2p54>. In addition, a numerical method for maximum likelihood estimation is provided.

This package provides functions and classes to store, manipulate and summarise Monte Carlo Markov Chain (MCMC) samples. For more information see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.

Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.

This package provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.

Exploration and analysis of compositional data in the framework of Aitchison (1986, ISBN: 978-94-010-8324-9). This package provides tools for chemical fingerprinting and source tracking of ancient materials.

Win ratio approach to partially ordered data, such as multivariate ordinal responses under product (consensus) or prioritized order. Two-sample tests and multiplicative regression models are implemented (Mao, 2024, under revision).

The Penn World Table 10.x (<https://www.rug.nl/ggdc/productivity/pwt/>) provides information on relative levels of income, output, input, and productivity for 183 countries between 1950 and 2019.

Looks for amino acid and/or nucleotide patterns and/or small ligands coordinated to a given prosthetic centre. Files have to be in the local file system and contain proper extension.

Implementation of the SIMEX-Algorithm by Cook & Stefanski (1994) <doi:10.1080/01621459.1994.10476871> and MCSIMEX by Küchenhoff, Mwalili & Lesaffre (2006) <doi:10.1111/j.1541-0420.2005.00396.x>.

Time series toolkit with identical behavior for all time series classes: ts','xts', data.frame', data.table', tibble', zoo', timeSeries', tsibble', tis or irts'. Also converts reliably between these classes.

Simulates typing of R script files for presentations and demonstrations. Provides character-by-character animation with optional live code execution. Supports R scripts (.R), R Markdown (.Rmd), and Quarto (.qmd) documents.

Seasonal unit roots and seasonal stability tests. P-values based on response surface regressions are available for both tests. P-values based on bootstrap are available for seasonal unit root tests.

Analysing vital statistics based on tools consistent with the tidyverse. Tools are provided for data visualization, life table calculations, computing net migration numbers, Lee-Carter modelling; functional data modelling and forecasting.

Data in multidimensional systems is obtained from operational systems and is transformed to adapt it to the new structure. Frequently, the operations to be performed aim to transform a flat table into a ROLAP (Relational On-Line Analytical Processing) star database. The main objective of the package is to allow the definition of these transformations easily. The implementation of the multidimensional database obtained can be exported to work with multidimensional analysis tools on spreadsheets or relational databases.

Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see <doi:10.1016/j.ins.2016.11.002> and <doi:10.1007/s00500-016-2430-5>). The proposed package contains a set of methods that facilitates the development of LDCP systems. It main goal is increasing the visibility and practical use of this research line.

Convex Least Squares Programming (CLSP) is a two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems. It combines pseudoinverse-based estimation with convex-programming correction methods inspired by Lasso, Ridge, and Elastic Net to ensure numerical stability, constraint enforcement, and interpretability. The package also provides numerical stability analysis and CLSP-specific diagnostics, including partial R^2, normalized RMSE (NRMSE), Monte Carlo t-tests for mean NRMSE, and condition-number-based confidence bands.

This package provides functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.1080/10618600.2023.2231051> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.

Cross-Linguistic Data Format (CLDF) is a framework for storing cross-linguistic data, ensuring compatibility and ease of data exchange between different linguistic datasets see Forkel et al. (2018) <doi:10.1038/sdata.2018.205>. The rcldf package is designed to facilitate the manipulation and analysis of these datasets by simplifying the loading, querying, and visualisation of CLDF datasets making it easier to conduct comparative linguistic analyses, manage language data, and apply statistical methods directly within R.

Create, read and write GEXF (Graph Exchange XML Format) graph files (used in Gephi and others). It allows the user to easily build/read graph files including attributes, GEXF visual attributes (such as color, size, and position), network dynamics (for both edges and nodes) and edge weighting. Users can build/handle graphs element-by-element or massively through data-frames, visualize the graph on a web browser through gexf-js (a JavaScript library) and interact with the igraph package.

Point and interval estimation of linear parameters with data obtained from complex surveys (including stratified and clustered samples) when randomization techniques are used. The randomized response technique was developed to obtain estimates that are more valid when studying sensitive topics. Estimators and variances for 14 randomized response methods for qualitative variables and 7 randomized response methods for quantitative variables are also implemented. In addition, some data sets from surveys with these randomization methods are included in the package.

This package provides tools for estimating variance-mean dependence in count data from high-throughput genetic sequencing assays and for testing for differential expression based on a model using the negative binomial distribution.

This is a package for multivariate data analysis and graphical display of microarray data. Functions are included for supervised dimension reduction (between group analysis) and joint dimension reduction of two datasets (coinertia analysis).