This package provides a chess program which allows the user to create a game, add moves, check for legal moves and game result, plot the board, take back, read and write FEN (Forsythâ Edwards Notation). A basic chess engine based on minimax is implemented.

This package provides functions for the input/output and visualization of medical imaging data in the form of CIFTI files <https://www.nitrc.org/projects/cifti/>.

Cobb's maximum likelihood method for cusp-catastrophe modeling (Grasman, van der Maas, and Wagenmakers (2009) <doi:10.18637/jss.v032.i08>; Cobb (1981), Behavioral Science, 26(1), 75-78). Includes a cusp() function for model fitting, and several utility functions for plotting, and for comparing the model to linear regression and logistic curve models.

Calculate with spectral properties of light sources, materials, cameras, eyes, and scanners. Build complex systems from simpler parts using a spectral product algebra. For light sources, compute CCT, CRI, SSI, and IES TM-30 reports. For object colors, compute optimal colors and Logvinenko coordinates. Work with the standard CIE illuminants and color matching functions, and read spectra from text files, including CGATS files. Estimate a spectrum from its response. A user guide and 9 vignettes are included.

This package implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020) <doi:10.1007/s11222-020-09940-y>.

This package provides functions to generate frequency tables and apply cell key perturbation to protect against statistical disclosure in tabular outputs. The implemented methods are described in "Cell Key Perturbation User Guide" <https://github.com/ONSdigital/cell-key-perturbation-R/blob/main/documentation/SML_UserDoc_CKP_R.md>. Developed at the UK Office for National Statistics.

Calculating silhouette information for clusters on circular or linear data using fast algorithms. These algorithms run in linear time on sorted data, in contrast to quadratic time by the definition of silhouette. When used together with the fast and optimal circular clustering method FOCC (Debnath & Song 2021) <doi:10.1109/TCBB.2021.3077573> implemented in R package OptCirClust', circular silhouette can be maximized to find the optimal number of circular clusters; it can also be used to estimate the period of noisy periodical data.

Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.

This package provides a compositional mediation model for continuous outcome and binary outcomes to deal with mediators that are compositional data. Lin, Ziqiang et al. (2022) <doi:10.1016/j.jad.2021.12.019>.

This package provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) <doi:10.1111/biom.13716>, Robertson et al. (2021) <doi:10.48550/arXiv.2104.05905>, and Wang et al. (2024) <doi:10.48550/arXiv.2402.02684>. The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects and subgroup treatment effects. See Wang et al. (2025) <doi:10.1017/rsm.2025.5> for a detailed guide on using the package.

Identification of cardinal dates (begin, time of maximum, end of mass developments) in ecological time series using fitted Weibull functions.

Computes a composite year-on-year index for bank performance assessment using the CAMEL framework (Capital Adequacy, Asset Quality, Management Efficiency, Earnings, Liquidity). The multivariate weighting scheme employs factor analysis with robust covariance estimation to derive communality-based weights from the correlation matrix of CAMEL ratios. Provides functions for index computation, visualization, and comparison across banks and time periods.The methodology is described in Ayimah et al. (2023a) <doi:10.9734/bpi/mono/978-81-19315-32-1> and Ayimah et al. (2023b) <https://ajtem.com/index.php/ajtem/article/view/53>.

Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on future (Bengtsson, 2024 <doi:10.32614/CRAN.package.future>) and mirai (Gao et al., 2025 <doi:10.32614/CRAN.package.mirai>) parallel back-ends, terra (Hijmans et al., 2025 <doi:10.32614/CRAN.package.terra>) and sf (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_*() functions to par_grid(), par_hierarchy(), or par_multirasters() functions. Virtually any functions accepting classes in terra or sf packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at(), which uses exactextractr (Baston, 2023 <doi:10.32614/CRAN.package.exactextractr>), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw()) and summation of exponentially decaying weights (summarize_sedc()) are also provided.

Calculates confidence intervals after variable selection using repeated data splits. The package offers methods to address the challenges of post-selection inference, ensuring more accurate confidence intervals in models involving variable selection. The two main functions are lmps', which records the different models selected across multiple data splits as well as the corresponding coefficient estimates, and cips', which takes the lmps object as input to select variables and perform inferences using two types of voting.

The goal of cvsem is to provide functions that allow for comparing Structural Equation Models (SEM) using cross-validation. Users can specify multiple SEMs using lavaan syntax. cvsem computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) <doi:10.18637/jss.v048.i02>. The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors.

Computational tools for confidence-distribution-propagation-based inference in random-effects meta-analysis. Implements confidence-distribution propagation for frequentist inference in random-effects meta-analysis. The package samples the between-study variance from a confidence distribution based on the exact distribution of Cochran's Q, samples the average effect conditionally on each draw, and generates the true effect in a future study. It provides prediction intervals and confidence intervals for the average effect, between-study variance, between-study standard deviation, and I2. The methods are described in Noma and Schwarzer (2026) <doi:10.48550/arXiv.2608.26527>.

Cristin to Zotero ('c2z') aims at obtaining total dominion over Cristin ('Current Research Information SysTem in Norway') and Zotero'. The package enables manipulating Zotero libraries using R'. Import references from Cristin', Regjeringen', CRAN', ISBN ('Alma', LoC'), and DOI ('CrossRef', DataCite') to a Zotero library. Add, edit, copy, or delete items, including attachments and collections, and export references to BibLaTeX (and other formats).

Datasets related to the Comrades Marathon used in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics". The main dataset contains the times of every runner that finished in the time limit for each year the race was run.

Connect and pull data from the CJA API, which powers CJA Workspace <https://github.com/AdobeDocs/cja-apis>. The package was developed with the analyst in mind and will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. New features are actively being developed and we value your feedback and contribution to the process.

Fit and apply ComBat, linear mixed-effects models (LMM), or prescaling to harmonize magnetic resonance imaging (MRI) data from different sites. Briefly, these methods remove differences between sites due to using different scanning devices, and LMM additionally tests linear hypotheses. As detailed in the manual, the original ComBat function was first modified for the harmonization of MRI data (Fortin et al. (2017) <doi:10.1016/j.neuroimage.2017.11.024>) and then modified again to create separate functions for fitting and applying the harmonization and allow missing values and constant rows for its use within the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium (Radua et al. (2020) <doi:10.1016/j.neuroimage.2020.116956>); this package includes the latter version. LMM calls "lme" massively considering specific brain imaging details. Finally, prescaling is a good option for fMRI, where different devices can have varying units of measurement.

This package provides functions for performing experimental comparisons of algorithms using adequate sample sizes for power and accuracy. Implements the methodology originally presented in Campelo and Takahashi (2019) <doi:10.1007/s10732-018-9396-7> for the comparison of two algorithms, and later generalised in Campelo and Wanner (Submitted, 2019) <arxiv:1908.01720>.

Estimation of optimal portfolio weights as combination of simple portfolio strategies, like the tangency, global minimum variance (GMV) or naive (1/N) portfolio. It is based on a utility maximizing 8-fund rule. Popular special cases like the Kan-Zhou(2007) 2-fund and 3-fund rule or the Tu-Zhou(2011) estimator are nested.

Produce forest plots to visualize covariate effects using either the command line or an interactive Shiny application.

Routines for the graphical representation of correlation matrices by means of correlograms, MDS maps and biplots obtained by PCA, PFA or WALS (weighted alternating least squares); See Graffelman & De Leeuw (2023) <doi: 10.1080/00031305.2023.2186952>.