This package provides implementations of origin-based and symmetrized minimum covariance determinant (MCD) estimators, together with supporting utility functions.

This package provides image-related C/C++ header files from the stb single-file libraries for image loading, writing, and resizing.

Offers Bayesian semiparametric density estimation and tail-index estimation for heavy tailed data, by using a parametric, tail-respecting transformation of the data to the unit interval and then modeling the transformed data with a purely nonparametric logistic Gaussian process density prior. Based on Tokdar et al. (2022) <doi:10.1080/01621459.2022.2104727>.

Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics.

This package provides functions for calculating species richness for rarefaction and extrapolation, primarily non-parametric species richness such as jackknife, Chao1, and ACE. Also available are functions for plotting species richness and extrapolation curves, and computing standard diversity and entropy indices.

Easily display user feedback in Shiny apps.

In base R, object attributes are lost when objects are modified by common data operations such as subset, filter, slice, append, extract etc. This packages allows objects to be marked as sticky and have attributes persisted during these operations or when inserted into or extracted from list-like or table-like objects.

Generates multiple imputed datasets from a substantive model compatible fully conditional specification model for time-to-event data. Our method assumes that the censoring process also depends on the covariates with missing values. Details will be available in an upcoming publication.

This package provides methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) <doi:10.1016/j.catena.2023.106927> and Trevisani and Guth (2025) <doi:10.3390/rs17233864>). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2 and of order 4. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices.

Use behavioural variables to score activity and infer sleep from bouts of immobility. It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data. It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) <DOI:10.1016/S0896-6273(00)80877-6>), activity classification for Ethoscopes (Geissmann et al. (2017) <DOI:10.1371/journal.pbio.2003026>) and a new algorithm to detect when animals are dead.

An assortment of helper functions for doing structural equation modeling, mainly by lavaan for now. Most of them are time-saving functions for common tasks in doing structural equation modeling and reading the output. This package is not for functions that implement advanced statistical procedures. It is a light-weight package for simple functions that do simple tasks conveniently, with as few dependencies as possible.

Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods.

This package implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the SemNetDictionaries package to make the cleaning process more accurate, efficient, and reproducible.

An efficient tool for fitting the nested common and shared atoms models using variational Bayes approximate inference for fast computation. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, Dâ Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>.

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 a facility to generate balanced semi-Latin rectangles with any cell size (preferably up to ten) with given number of treatments, see Uto, N.P. and Bailey, R.A. (2020). "Balanced Semi-Latin rectangles: properties, existence and constructions for block size two". Journal of Statistical Theory and Practice, 14(3), 1-11, <doi:10.1007/s42519-020-00118-3>. It also provides facility to generate partially balanced semi-Latin rectangles for cell size 2, 3 and 4 for any number of treatments.

Detection of outliers and influential errors using a latent variable model.

Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in <doi:10.1007/s10109-009-0100-1> and <doi:10.1080/13658816.2011.586327>.

Send email using Sendgrid <https://sendgrid.com/> mail API(v3) <https://docs.sendgrid.com/api-reference/how-to-use-the-sendgrid-v3-api/authentication>.

This package provides functions for computing a standardized moderation effect in moderated regression and forming its confidence interval by nonparametric bootstrapping as proposed in Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Also includes simple-to-use functions for computing conditional effects (unstandardized or standardized) and plotting moderation effects.

This package provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the mapproj package.

Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream validation techniques such as cross-validation to be applied to each set without concern for overfitting.

Spatial model calculation for static and dynamic panel data models, weights matrix creation and Bayesian model comparison. Bayesian model comparison methods were described by LeSage (2014) <doi:10.1016/j.spasta.2014.02.002>. The Lee'-'Yu transformation approach is described in Yu', De Jong and Lee (2008) <doi:10.1016/j.jeconom.2008.08.002>, Lee and Yu (2010) <doi:10.1016/j.jeconom.2009.08.001> and Lee and Yu (2010) <doi:10.1017/S0266466609100099>.

This package provides predictive accuracy tools to evaluate time-to-event survival models. This includes calculating the concordance probability estimate that incorporates the follow-up time for a particular study developed by Devlin, Gonen, Heller (2020)<doi:10.1007/s10985-020-09503-3>. It also evaluates the concordance probability estimate for nested Cox proportional hazards models using a projection-based approach by Heller and Devlin (under review).