This package provides a wrapper for Blizzard's Starcraft II (a 2010 real-time strategy game) Application Programming Interface (API). All documented API calls are implemented in an easy-to-use and consistent manner.

Import, create and assemble data needed to fit spatial-statistical stream-network models using the SSN2 package for R'. Streams, observations, and prediction locations are represented as simple features and specific tools provided to define topological relationships between features; calculate the hydrologic distances (with flow-direction preserved) and the spatial additive function used to weight converging stream segments; and export the topological, spatial, and attribute information to an `SSN` (spatial stream network) object, which can be efficiently stored, accessed and analysed in R'. A detailed description of methods used to calculate and format the spatial data can be found in Peterson, E.E. and Ver Hoef, J.M., (2014) <doi:10.18637/jss.v056.i02>.

This package provides a wrapper to access data from the SeeClickFix web API for R. SeeClickFix is a central platform employed by many cities that allows citizens to request their city's services. This package creates several functions to work with all the built-in calls to the SeeClickFix API. Allows users to download service request data from numerous locations in easy-to-use dataframe format manipulable in standard R functions.

The President of the United States is constitutionally obligated to provide a report known as the State of the Union'. The report summarizes the current challenges facing the country and the president's upcoming legislative agenda. While historically the State of the Union was often a written document, in recent decades it has always taken the form of an oral address to a joint session of the United States Congress. This package provides the raw text from every such address with the intention of being used for meaningful examples of text analysis in R. The corpus is well suited to the task as it is historically important, includes material intended to be read and material intended to be spoken, and it falls in the public domain. As the corpus spans over two centuries it is also a good test of how well various methods hold up to the idiosyncrasies of historical texts. Associated data about each address, such as the year, president, party, and format, are also included.

This package provides methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects.

This package provides Sensory and Consumer Data mapping and analysis <doi:10.14569/IJACSA.2017.081266>. The mapping visualization is made available from several features : options in dimension reduction methods and prediction models ranging from linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm is available. A selection process of map stability is provided. A shiny application is included. It presents an easy GUI for the implemented functions as well as a comparative tool of fit models using several criteria. Basic analysis such as characterization of products, panelists and sessions likewise consumer segmentation are also made available.

Implementations self-normalization (SN) based algorithms for change-points estimation in time series data. This comprises nested local-window algorithms for detecting changes in both univariate and multivariate time series developed in Zhao, Jiang and Shao (2022) <doi:10.1111/rssb.12552>.

This package provides tools for making, retrieving, displaying and solving sudoku games. This package is an alternative to the earlier sudoku-solver package, sudoku'. The present package uses a slightly different algorithm, has a simpler coding and presents a few more sugar tools, such as plot and print methods. Solved sudoku games are of some interest in Experimental Design as examples of Latin Square designs with additional balance constraints.

The Simulation-based Sampling Protocol (SSP) is an R package designed to estimate sampling effort in studies of ecological communities. It is based on the concept of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015, <doi:10.1111/ele.12385>) and the simulation of ecological data. The theoretical background is described in Guerra-Castro et al. (2020, <doi:10.1111/ecog.05284>).

Implementation for sparse logistic functional principal component analysis (SLFPCA). SLFPCA is specifically developed for functional binary data, and the estimated eigenfunction can be strictly zero on some sub-intervals, which is helpful for interpretation. The crucial function of this package is SLFPCA().

Get started with new projects by dropping a skeleton of a new project into a new or existing directory, initialise git repositories, and create reproducible environments with the renv package. The package allows for dynamically named files, folders, file content, as well as the functionality to drop individual template files into existing projects.

Simulation of recurrent event data for non-constant baseline hazard in the total time model with risk-free intervals and possibly a competing event. Possibility to cut the data to an interim data set. Data can be plotted. Details about the method can be found in Jahn-Eimermacher, A. et al. (2015) <doi:10.1186/s12874-015-0005-2>.

Set of functions to quantify and map the behaviour of winds generated by tropical storms and cyclones in space and time. It includes functions to compute and analyze fields such as the maximum sustained wind field, power dissipation index and duration of exposure to winds above a given threshold. It also includes functions to map the trajectories as well as characteristics of the storms.

Adds support for the English language to the sylly package. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).

This package provides methods for regression with high-dimensional predictors and univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.

Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.

An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.

This package creates an S4 class "SSM" and defines functions for fitting smooth supersaturated models, a polynomial model with spline-like behaviour. Functions are defined for the computation of Sobol indices for sensitivity analysis and plotting the main effects using FANOVA methods. It also implements the estimation of the SSM metamodel error using a GP model with a variety of defined correlation functions.

This package provides tools to conduct interpretable sensitivity analyses for weighted estimators, introduced in Huang (2024) <doi:10.1093/jrsssa/qnae012> and Hartman and Huang (2024) <doi:10.1017/pan.2023.12>. The package allows researchers to generate the set of recommended sensitivity summaries to evaluate the sensitivity in their underlying weighting estimators to omitted moderators or confounders. The tools can be flexibly applied in causal inference settings (i.e., in external and internal validity contexts) or survey contexts.

Analysis of spatial relationships between cell types in spatial transcriptomics data. Spatial proximity is a critical factor in cell-cell communication. The package calculates nearest neighbor distances between specified cell types and provides visualization tools to explore spatial patterns. Applications include studying cell-cell interactions, immune microenvironment characterization, and spatial organization of tissues.

This package provides a graphical user interface (GUI) for fitting Bayesian regression models using the package brms which in turn relies on Stan (<https://mc-stan.org/>). The shinybrms GUI is a shiny app.

The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" <arXiv:1911.05461>; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory".

Download and read datasets from the Swiss National Science Foundation (SNF, FNS, SNSF; <https://snf.ch>). The package is lightweight and without dependencies. Downloaded data can optionally be cached, to avoid repeated downloads of the same files. There are also utilities for comparing different versions of datasets, i.e. to report added, removed and changed entries.

Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo for checking the case-crossover code.