Seamless extraction of river networks from digital elevation models data. The package allows analysis of digital elevation models that can be either externally provided or downloaded from open source repositories (thus interfacing with the elevatr package). Extraction is performed via the D8 flow direction algorithm of TauDEM (Terrain Analysis Using Digital Elevation Models), thus interfacing with the traudem package. Resulting river networks are compatible with functions from the OCNet package. See Carraro (2023) <doi:10.5194/hess-27-3733-2023> for a presentation of the package.

Enhances the R Optimization Infrastructure ('ROI') package with the SCS solver for solving convex cone problems.

An implementation of robust bent line regression. It can fit the bent line regression and test the existence of change point, for the paper, "Feipeng Zhang and Qunhua Li (2016). Robust bent line regression, submitted.".

Inspired by the classic RSA', we developed the improved Generalized Reporter Score-based Analysis (GRSA) method, implemented in the R package ReporterScore', along with comprehensive visualization methods and pathway databases. GRSA is a threshold-free method that works well with all types of biomedical features, such as genes, chemical compounds, and microbial species. Importantly, the GRSA supports multi-group and longitudinal experimental designs, because of the included multi-group-compatible statistical methods.

An R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs.

This package provides tools for manipulating, exploring, and visualising multiple-response data, including scored or ranked responses. Conversions to and from factors, lists, strings, matrices; reordering, lumping, flattening; set operations; tables; frequency and co-occurrence plots.

Solves the individual bioenergetic balance for different aquaculture sea fish (Sea Bream and Sea Bass; Brigolin et al., 2014 <doi:10.3354/aei00093>) and shellfish (Mussel and Clam; Brigolin et al., 2009 <doi:10.1016/j.ecss.2009.01.029>; Solidoro et al., 2000 <doi:10.3354/meps199137>). Allows for spatialized model runs and population simulations.

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.

This package provides a single method implementing multiple approaches to generate pseudo-random vectors whose components sum up to one (see, e.g., Maziero (2015) <doi:10.1007/s13538-015-0337-8>). The components of such vectors can for example be used for weighting objectives when reducing multi-objective optimisation problems to a single-objective problem in the socalled weighted sum scalarisation approach.

Facilitating the creation of reproducible statistical report templates. Once created, rapport templates can be exported to various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the converter backend.

Transfer REDCap (Research Electronic Data Capture) data to a database, specifically optimized for DuckDB'. Processes data in chunks to handle large datasets without exceeding available memory. Features include data labeling, coded value conversion, and hearing a "quack" sound on success.

This package provides a robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. A specific weighting scheme is applied which avoids iterations, and leads to a highly efficient robust PLS estimator.

Residual balancing is a robust method of constructing weights for marginal structural models, which can be used to estimate (a) the average treatment effect in a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020 <doi:10.1017/pan.2020.2>). This package provides three functions, rbwPoint(), rbwMed(), and rbwPanel(), that produce residual balancing weights for estimating (a), (b), (c), respectively.

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.

Simplified scenario testing and sensitivity analysis, redesigned to use packages future and furrr'. Provides functions for generating function argument sets using one-factor-at-a-time (OFAT) and (sampled) permutations.

This package provides simplified methods for managing classic Rubik's cubes and many other modifications of it (such as NxNxN size cubes, void cubes and 8-coloured cubes - so called octa cubes). Includes functions of handling special syntax for managing such cubes; and different approach to plotting 3D cubes without using external libraries (for example OpenGL').

Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions. Includes transport routines in porous media, in estuaries, and in bodies with variable shape.

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.

Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data.

An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.

An R API Client for Valve's Dota2. RDota2 can be easily used to connect to the Steam API and retrieve data for Valve's popular video game Dota2. You can find out more about Dota2 at <http://store.steampowered.com/app/570/>.

Read, write and manipulate Praat TextGrid, PitchTier, Pitch, IntensityTier, Formant, Sound, and Collection files <https://www.fon.hum.uva.nl/praat/>.

Facilitates efficient polygon search using kd trees. Coordinate level spatial data can be aggregated to higher geographical identities like census blocks, ZIP codes or police district boundaries. This process requires mapping each point in the given data set to a particular identity of the desired geographical hierarchy. Unless efficient data structures are used, this can be a daunting task. The operation point.in.polygon() from the package sp is computationally expensive. Here, we exploit kd-trees as efficient nearest neighbor search algorithm to dramatically reduce the effective number of polygons being searched.

Traditional latent variable models assume that the population is homogeneous, meaning that all individuals in the population are assumed to have the same latent structure. However, this assumption is often violated in practice given that individuals may differ in their age, gender, socioeconomic status, and other factors that can affect their latent structure. The robust expectation maximization (REM) algorithm is a statistical method for estimating the parameters of a latent variable model in the presence of population heterogeneity as recommended by Nieser & Cochran (2023) <doi:10.1037/met0000413>. The REM algorithm is based on the expectation-maximization (EM) algorithm, but it allows for the case when all the data are generated by the assumed data generating model.