Apply sensitivity analysis for offline policy evaluation, as implemented in Jung et al. (2017) <arXiv:1702.04690> based on Rosenbaum and Rubin (1983) <http://www.jstor.org/stable/2345524>.

Enhances the R Optimization Infrastructure (ROI) package by registering the CPLEX commercial solver. It allows for solving mixed integer quadratically constrained programming (MIQPQC) problems as well as all variants/combinations of LP, QP, QCP, IP.

This package provides tools for working with rotational data, including simulation from the most commonly used distributions on SO(3), methods for different Bayes, mean and median type estimators for the central orientation of a sample, confidence/credible regions for the central orientation based on those estimators and a novel visualization technique for rotation data. Most recently, functions to identify potentially discordant (outlying) values have been added. References: Bingham, Melissa A. and Nordman, Dan J. and Vardeman, Steve B. (2009), Bingham, Melissa A and Vardeman, Stephen B and Nordman, Daniel J (2009), Bingham, Melissa A and Nordman, Daniel J and Vardeman, Stephen B (2010), Leon, C.A. and Masse, J.C. and Rivest, L.P. (2006), Hartley, R and Aftab, K and Trumpf, J. (2011), Stanfill, Bryan and Genschel, Ulrike and Hofmann, Heike (2013), Maonton, Jonathan (2004), Mardia, KV and Jupp, PE (2000, ISBN:9780471953333), Rancourt, D. and Rivest, L.P. and Asselin, J. (2000), Chang, Ted and Rivest, Louis-Paul (2001), Fisher, Nicholas I. (1996, ISBN:0521568900).

Enhances the R Optimization Infrastructure ('ROI') package with the NLopt solver for solving nonlinear optimization problems.

Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) <arXiv:2005.02930> for correcting and quantifying publication bias in univariate meta-analysis. Also fits standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.

We generate random variables following general Marchenko-Pastur distribution and Tracy-Widom distribution. We compute limits and distributions of eigenvalues and generalized components of spiked covariance matrices. We give estimation of all population eigenvalues of spiked covariance matrix model. We give tests of population covariance matrix. We also perform matrix denoising for signal-plus-noise model.

Various tools for handling fuzzy measures, calculating Shapley value and interaction index, Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using lpsolve package, whose source in C adapted to the R environment is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package. Please refer to the following: [1] <https://personal-sites.deakin.edu.au/~gleb/fmtools.html> [2] G. Beliakov, H. Bustince, T. Calvo, A Practical Guide to Averaging', Springer, (2016, ISBN: 978-3-319-24753-3). [3] G. Beliakov, S. James, J-Z. Wu, Discrete Fuzzy Measures', Springer, (2020, ISBN: 978-3-030-15305-2).

Data sets are often corrupted by outliers. When data are multivariate outliers can be classified as case-wise or cell-wise. The latters are particularly challenge to handle. We implement a robust estimation procedure for Seemingly Unrelated Regression Models which is able to cope well with both type of outliers. Giovanni Saraceno, Fatemah Alqallaf, Claudio Agostinelli (2021) <doi:10.48550/arXiv.2107.00975>.

This package provides a common framework for calculating distance matrices.

We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).

This package provides a flexible framework for implementing hierarchical access control in shiny applications. Features include user permission management through a two-tier system of access panels and units, pluggable shiny module for administrative interfaces, and support for multiple storage backends (local, AWS S3', Posit Connect'). The system enables fine-grained control over application features, with built-in audit trails and user management capabilities. Integrates seamlessly with Posit Connect's authentication system.

This package provides a simple data science challenge system using R Markdown and Dropbox <https://www.dropbox.com/>. It requires no network configuration, does not depend on external platforms like e.g. Kaggle <https://www.kaggle.com/> and can be easily installed on a personal computer.

Allows the user to access functionality in the CDK', a Java framework for cheminformatics. This allows the user to load molecules, evaluate fingerprints, calculate molecular descriptors and so on. In addition, the CDK API allows the user to view structures in 2D.

Allows one to use Osmium Tool (<https://osmcode.org/osmium-tool/>) from R. Osmium is a multipurpose command line tool that enables one to manipulate and analyze OpenStreetMap files through several different commands. Currently, this package does not aim to offer functions that cover the entire Osmium API, instead making available functions that wrap only a very limited set of its features.

Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).

Construct an explainable nomogram for a machine learning (ML) model to improve availability of an ML prediction model in addition to a computer application, particularly in a situation where a computer, a mobile phone, an internet connection, or the application accessibility are unreliable. This package enables a nomogram creation for any ML prediction models, which is conventionally limited to only a linear/logistic regression model. This nomogram may indicate the explainability value per feature, e.g., the Shapley additive explanation value, for each individual. However, this package only allows a nomogram creation for a model using categorical without or with single numerical predictors. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rmlnomogram/blob/master/doc/ml_nomogram_exemplar.html>.

Duplicated restaurant data (pre-processed and formatted) for entity resolution. This package contains formatted data from a data set that contains information about different restaurants, with the Zagats portion containing 331 records and the Fodors portion containing 533 records. The following variables are included in the data set: id, name, address, city, phone, type. The data set has a respective gold data set that provides information on which records match based on id.

An HTTP API client for Lemmy (<https://github.com/LemmyNet/lemmy>) in R. Code and documentation are generated from the official JavaScript client source (<https://github.com/LemmyNet/lemmy-js-client>).

TROLL is coded in C++ and it typically simulates hundreds of thousands of individuals over hundreds of years. The rcontroll R package is a wrapper of TROLL'. rcontroll includes functions that generate inputs for simulations and run simulations. Finally, it is possible to analyse the TROLL outputs through tables, figures, and maps taking advantage of other R visualisation packages. rcontroll also offers the possibility to generate a virtual LiDAR point cloud that corresponds to a snapshot of the simulated forest.

Ray Shooting Depth functions are provided for bivariate analysis. This mainly includes functions for computing the bivariate depth as well as RS median. Drawing functions for depth bags are also provided.

This package performs Wavelet Lifting Transforms focusing on signal denoising and functional data analysis (FDA). Implements a hybrid architecture with a zero-allocation C++ core for high-performance processing. Features include unified offline (batch) denoising, causal (real-time) filtering using a ring buffer engine, and adaptive recursive thresholding.

Perform wavelet analysis (orthogonal,translation invariant, tensorial, 1-2-3d transforms, thresholding, block thresholding, linear,...) with applications to data compression or denoising/regression. The core of the code is a port of MATLAB Wavelab toolbox written by D. Donoho, A. Maleki and M. Shahram (<https://statweb.stanford.edu/~wavelab/>).

SurveyCTO is a platform for mobile data collection in offline settings. The rsurveycto R package uses the SurveyCTO REST API <https://docs.surveycto.com/05-exporting-and-publishing-data/05-api-access/01.api-access.html> to read datasets and forms from a SurveyCTO server into R as data.table's and to download file attachments. The package also has limited support to write datasets to a server.

This package provides random number generating functions that are much more context aware than the built-in functions. The functions are also much safer, as they check for incompatible values, and more reproducible.