This package provides functions to read and write neuroimaging data in various file formats, with a focus on FreeSurfer <http://freesurfer.net/> formats. This includes, but is not limited to, the following file formats: 1) MGH/MGZ format files, which can contain multi-dimensional images or other data. Typically they contain time-series of three-dimensional brain scans acquired by magnetic resonance imaging (MRI). They can also contain vertex-wise measures of surface morphometry data. The MGH format is named after the Massachusetts General Hospital, and the MGZ format is a compressed version of the same format. 2) FreeSurfer morphometry data files in binary curv format. These contain vertex-wise surface measures, i.e., one scalar value for each vertex of a brain surface mesh. These are typically values like the cortical thickness or brain surface area at each vertex. 3) Annotation file format. This contains a brain surface parcellation derived from a cortical atlas. 4) Surface file format. Contains a brain surface mesh, given by a list of vertices and a list of faces.

Enables filtering datasets by a prior specified identifiers which correspond to saved filter expressions.

Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) <doi:10.1109/JSTSP.2016.2539100> for an overview of the topic.

Interface for accessing the Fortnite Ecosystem API, allowing users to retrieve island metadata and engagement metrics. The package provides functions to search for Fortnite Creative islands, retrieve detailed metadata about specific islands including titles, descriptions, and tags, and access engagement metrics such as daily active users and play duration. It supports pagination for large result sets and time-series analysis of island performance. The API endpoint is <https://api.fortnite.com/ecosystem/v1>.

This package provides a collection of functions for trading and rebalancing financial instruments. It implements various technical indicators to analyse time series such as moving averages or stochastic oscillators.

Enhances the functionality of the mvbutils::foodweb() program. The matrix-format output of the original program contains identical row names and column names, each name representing a retrieved function. This format is enhanced by using the find_funs() program [see Sebastian (2017) <https://sebastiansauer.github.io/finds_funs/>] to concatenate the package name to the function name. Each package is assigned a unique color, that is used to color code the text naming the packages and the functions. This color coding is extended to the entries of value "1" within the matrix, indicating the pattern of ancestor and descendent functions.

Returns the noncentrality parameter of the noncentral F distribution if probability of type I and type II error, degrees of freedom of the numerator and the denominator are given. It may be useful for computing minimal detectable differences for general ANOVA models. This program is documented in the paper of A. Baharev, S. Kemeny, On the computation of the noncentral F and noncentral beta distribution; Statistics and Computing, 2008, 18 (3), 333-340.

This package provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.

Interface to Palantir Foundry', including reading and writing structured or unstructured datasets, and more <https://www.palantir.com/platforms/foundry/>.

This package provides a programmatic interface to the Finnish Biodiversity Information Facility ('FinBIF') API (<https://api.laji.fi>). FinBIF aggregates Finnish biodiversity data from multiple sources in a single open access portal for researchers, citizen scientists, industry and government. FinBIF allows users of biodiversity information to find, access, combine and visualise data on Finnish plants, animals and microorganisms. The finbif package makes the publicly available data in FinBIF easily accessible to programmers. Biodiversity information is available on taxonomy and taxon occurrence. Occurrence data can be filtered by taxon, time, location and other variables. The data accessed are conveniently preformatted for subsequent analyses.

An interactive shiny'-based tool for exploration and quality assurance and quality control (QA/QC) of eddy covariance flux tower data processing. It generates data-point removal code via user-directed selection from a scatterplot, and can export a cleaned .csv with removed points set to NA plus an R script for reproducibility. Reference: Key (2025) <DOI:10.5281/zenodo.15597159>.

Three methods are implemented in R to facilitate the aggregations of flags in official statistics. From the underlying flags the highest in the hierarchy, the most frequent, or with the highest total weight is propagated to the flag(s) for EU or other aggregates. Below there are some reference documents for the topic: <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_v2_1.docx>, <https://sdmx.org/wp-content/uploads/CL_CONF_STATUS_1_2_2018.docx>, <http://ec.europa.eu/eurostat/data/database/information>, <http://www.oecd.org/sdd/33869551.pdf>, <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_implementation_20-10-2014.pdf>.

This package contains regional Floristic Quality Assessment databases that have been approved or approved with reservations by the U.S. Army Corps of Engineers (USACE). Paired with the fqacalc R package, these data sets allow for Floristic Quality Assessment metrics to be calculated. For information on FQA see Spyreas (2019) <doi:10.1002/ecs2.2825>. Both packages were developed for the USACE by the U.S. Army Engineer Research and Development Center's Environmental Laboratory.

Multidimensional scaling (MDS) functions for various tasks that are beyond the beta stage and way past the alpha stage. Currently, options are available for weights, restrictions, classical scaling or principal coordinate analysis, transformations (linear, power, Box-Cox, spline, ordinal), outlier mitigation (rdop), out-of-sample estimation (predict), negative dissimilarities, fast and faster executions with low memory footprints, penalized restrictions, cross-validation-based penalty selection, supplementary variable estimation (explain), additive constant estimation, mixed measurement level distance calculation, restricted classical scaling, etc. More will come in the future. References. Busing (2024) "A Simple Population Size Estimator for Local Minima Applied to Multidimensional Scaling". Manuscript submitted for publication. Busing (2025) "Node Localization by Multidimensional Scaling with Iterative Majorization". Manuscript submitted for publication. Busing (2025) "Faster Multidimensional Scaling". Manuscript in preparation. Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication.

Integrated Functional Depth for Partially Observed Functional Data and applications to visualization, outlier detection and classification. It implements the methods proposed in: Elà as, A., Jiménez, R., Paganoni, A. M. and Sangalli, L. M., (2023), "Integrated Depth for Partially Observed Functional Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2022.2070171>. Elà as, A., Jiménez, R., & Shang, H. L. (2023), "Depth-based reconstruction method for incomplete functional data", Computational Statistics, <doi:10.1007/s00180-022-01282-9>. Elà as, A., Nagy, S. (2024), "Statistical properties of partially observed integrated functional depths", TEST, <doi:10.1007/s11749-024-00954-6>.

This package provides a full set of fast data manipulation tools with a tidy front-end and a fast back-end using collapse and cheapr'.

This package provides a collection of functions to manage, to investigate and to analyze data sets of financial assets from different points of view.

One can easily draw the membership function of f(x,y) by package FuzzyNumbers.Ext.2 in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package FuzzyNumbers for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result.

Use spectrophotometry measurements performed on insects as a way to infer pathogens virulence. Insect movements cause fluctuations in fluorescence signal, and functions are provided to estimate when the insect has died as the moment when variance in autofluorescence signal drops to zero. The package provides functions to obtain this estimate together with functions to import spectrophotometry data from a Biotek microplate reader. Details of the method are given in Parthuisot et al. (2018) <doi:10.1101/297929>.

S4 classes for univariate and multivariate functional data with utility functions. See <doi:10.18637/jss.v093.i05> for a detailed description of the package functionalities and its interplay with the MFPCA package for multivariate functional principal component analysis <https://CRAN.R-project.org/package=MFPCA>.

This package provides tools for estimating causal effects in panel data using counterfactual methods, as well as other modern DID estimators. It is designed for causal panel analysis with binary treatments under the parallel trends assumption. The package supports scenarios where treatments can switch on and off and allows for limited carryover effects. It includes several imputation estimators, such as Gsynth (Xu 2017), linear factor models, and the matrix completion method. Detailed methodology is described in Liu, Wang, and Xu (2024) <doi:10.48550/arXiv.2107.00856> and Chiu et al. (2025) <doi:10.48550/arXiv.2309.15983>. Optionally integrates with the "HonestDiDFEct" package for sensitivity analyses compatible with imputation estimators. "HonestDiDFEct" is not on CRAN but can be obtained from <https://github.com/lzy318/HonestDiDFEct>.

It calculates the alpha-quantile proposed by Daouia and Simar (2007) <doi:10.1016/j.jeconom.2006.07.002> and order-m efficiency score in multi-dimension proposed by Daouia and Gijbels (2011) <doi:10.1016/j.jeconom.2010.12.002> and computes several summaries and representation of the associated frontiers in 2d and 3d.

This package provides a research estimation tool for analysts that work with sample-based inventory data from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program.

This package implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.