This package provides tools for Markov chain Monte Carlo (MCMC) and Maximum A Posteriori (MAP) estimation utilizing the RTMB package. It supports various statistical models including generalized linear mixed models, factor analysis, item response theory, and multidimensional unfolding. The package allows users to easily transition between frequentist and Bayesian paradigms using a unified interface. Automatic differentiation and Laplace approximation follow Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, and MCMC sampling uses the No-U-Turn Sampler described by Hoffman and Gelman (2014) <https://jmlr.org/papers/v15/hoffman14a.html>.

Generates confidence intervals for standardized regression coefficients using delta method standard errors for models fitted by lm() as described in Yuan and Chan (2011) <doi:10.1007/s11336-011-9224-6> and Jones and Waller (2015) <doi:10.1007/s11336-013-9380-y>. The package can also be used to generate confidence intervals for differences of standardized regression coefficients and as a general approach to performing the delta method. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

Typical morphological profiling datasets have millions of cells and hundreds of features per cell. When working with this data, you must clean the data, normalize the features to make them comparable across experiments, transform the features, select features based on their quality, and aggregate the single-cell data, if needed. cytominer makes these steps fast and easy. Methods used in practice in the field are discussed in Caicedo (2017) <doi:10.1038/nmeth.4397>. An overview of the field is presented in Caicedo (2016) <doi:10.1016/j.copbio.2016.04.003>.

We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation.

This package provides a flexible framework for Agent-Based Models (ABM), the epiworldR package provides methods for prototyping disease outbreaks and transmission models using a C++ backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents features, providing great complexity for the model dynamics. Furthermore, epiworldR is ideal for simulation studies featuring large populations.

This package contains miscellaneous functions useful for managing NetCDF files (see <https://en.wikipedia.org/wiki/NetCDF>), get moon phase and time for sun rise and fall, tide level, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.

Automate the detection of gaps and elevations in mapped sequencing read coverage using a 2D pattern-matching algorithm. ProActive detects, characterizes and visualizes read coverage patterns in both genomes and metagenomes. Optionally, users may provide gene annotations associated with their genome or metagenome in the form of a .gff file. In this case, ProActive will generate an additional output table containing the gene annotations found within the detected regions of gapped and elevated read coverage. Additionally, users can search for gene annotations of interest in the output read coverage plots.

Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.

This package provides distribution-free, model-agnostic prediction intervals for spatially and spatio-temporally dependent data using localized conformal calibration. Implements locally weighted split conformal prediction for geostatistical (point-referenced) data based on spatial-distance kernels, and a neighbourhood-weighted conformal procedure for areal (lattice) data based on graph adjacency structures. Relaxes the standard exchangeability assumption using spatial proximity, following the localized conformal framework of Mao, Martin and Reich (2024) <doi:10.1080/01621459.2022.2147531>. Includes comprehensive spatial diagnostic tools to audit empirical coverage, conditional spatial strata, and boundary proximity effects.

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors, both dynamic and static. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>. For the multivariate extension, see the shrinkTVPVAR package.

Data exploration and modelling is a process in which a lot of data artifacts are produced. Artifacts like: subsets, data aggregates, plots, statistical models, different versions of data sets and different versions of results. Archivist helps to store and manage artifacts created in R. It allows you to store selected artifacts as binary files together with their metadata and relations. Archivist allows sharing artifacts with others. It can look for already created artifacts by using its class, name, date of the creation or other properties. It also makes it easy to restore such artifacts.

Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.

This package provides high-performance tools for out-of-core text processing and data ingestion by leveraging system AWK utilities. Allows users to count records, filter rows, and compute streaming aggregationsâ such as group-by means, streaming medians, standard deviations, and correlationsâ directly on disk prior to reading data into R. By delegating line-by-line filtering and summarization to system-level AWK commands and streaming results back through data.table::fread(), the package significantly reduces memory footprint and execution times when working with large individual files or multi-file directory structures.

This package provides a simple tool to quantify the amount of transmission of an infectious disease of interest occurring within and between population groups. bumblebee uses counts of observed directed transmission pairs, identified phylogenetically from deep-sequence data or from epidemiological contacts, to quantify transmission flows within and between population groups accounting for sampling heterogeneity. Population groups might include: geographical areas (e.g. communities, regions), demographic groups (e.g. age, gender) or arms of a randomized clinical trial. See the bumblebee website for statistical theory, documentation and examples <https://magosil86.github.io/bumblebee/>.

Color values in R are often represented as strings of hexadecimal colors or named colors. This package offers fast conversion of these color representations to either an array of red/green/blue/alpha values or to the packed integer format used in native raster objects. Functions for conversion are also exported at the C level for use in other packages. This fast conversion of colors is implemented using an order-preserving minimal perfect hash derived from Majewski et al (1996) "A Family of Perfect Hashing Methods" <doi:10.1093/comjnl/39.6.547>.

This package provides utilities for scientific computation and visualisation, with an emphasis on applications in physics and astrophysics. Functionality includes random sampling from spherical and custom distributions, information and entropy analysis, Fourier transforms, two-point correlation estimation, binning and gridding of point sets, two-dimensional interpolation, Monte Carlo integration, vector operations, coordinate transformations, physical constants, and cosmological conversions. Graphics tools support the creation and export of publication-quality plots, animations, colour scales, map projections, and bitmap images. Several of these tools were used by Obreschkow et al. (2020) <doi:10.1093/mnras/staa445>.

This package provides a wrapper for the Julia package DiDInt.jl <https://ebjamieson97.github.io/DiDInt.jl/stable/> which implements intersection difference-in-differences (DID-INT), a method developed by Karim & Webb (2025) <doi:10.48550/arXiv.2412.14447>. Allows for unbiased estimation of the average effect of treatment on the treated (ATT) in cases when the common causal covariates assumption is violated. Also computes p-values for the ATT via the randomization inference procedure described in MacKinnon and Webb (2020) <doi:10.1016/j.jeconom.2020.04.024>.

This package provides fast moving-window ("focal") and buffer-based extraction for raster data using the terra package. Automatically selects between a C++ backend (via terra') and a Fast Fourier Transform (FFT) backend depending on problem size. The FFT backend supports sum and mean, while other statistics (e.g., median, min, max, standard deviation) are handled by the terra backend. Supports multiple kernel types (e.g., circle, rectangle, gaussian), with NA handling consistent with terra via na.rm and na.policy'. Operates on SpatRaster objects and returns results with the same geometry.

This package provides methods for estimating factor complexity coefficients in exploratory and confirmatory factor analysis (EFA/CFA) results. Included indices are the Hofman coefficient, Fleming's approach for factor simplicity, and others. Additional outputs include descriptive statistics (minimum, maximum, and mean) for target and non-target loadings, and visualization of results. References: Fleming, J.S. (2003) <doi:10.3758/bf03195531>; Hofmann, R.J. (1978) <doi:10.1207/s15327906mbr1302_9>; Kaiser, H.F. (1974) <doi:10.1007/BF02291575>; Bentler, P.M. (1977) <doi:10.1007/BF02294054>; Lorenzo-Seva, U. (2003) <doi:10.1007/BF02296652>.

Vitamin and mineral deficiencies continue to be a significant public health problem. This is particularly critical in developing countries where deficiencies to vitamin A, iron, iodine, and other micronutrients lead to adverse health consequences. Cross-sectional surveys are helpful in answering questions related to the magnitude and distribution of deficiencies of selected vitamins and minerals. This package provides tools for calculating and determining select vitamin and mineral deficiencies based on World Health Organization (WHO) guidelines found at <https://www.who.int/teams/nutrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system>.

The SoundexBR package provides an algorithm for decoding names into phonetic codes, as pronounced in Portuguese. The goal is for homophones to be encoded to the same representation so that they can be matched despite minor differences in spelling. The algorithm mainly encodes consonants; a vowel will not be encoded unless it is the first letter. The soundex code resultant consists of a four digits long string composed by one letter followed by three numerical digits: the letter is the first letter of the name, and the digits encode the remaining consonants.

Using the adjustment method from Benjamini & Hochberg (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x>, this package determines which variables are significant under repeated testing with a given dataframe of p values and an user defined "q" threshold. It then returns the original dataframe along with a significance column where an asterisk denotes a significant p value after FDR calculation, and NA denotes all other p values. This package uses the Benjamini & Hochberg method specifically as described in Lee, S., & Lee, D. K. (2018) <doi:10.4097/kja.d.18.00242>.

Reads, writes, and edits EXIF and other file metadata using ExifTool <https://exiftool.org/>, returning read results as a data frame. ExifTool supports many different metadata formats including EXIF, GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, Lyrics3, as well as the maker notes of many digital cameras by Canon, Casio, DJI, FLIR, FujiFilm, GE, GoPro, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung, Sanyo, Sigma/Foveon and Sony.

This package provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions.