This package provides a general framework for performing sparse functional clustering as originally described in Floriello and Vitelli (2017) <doi:10.1016/j.jmva.2016.10.008>, with the possibility of jointly handling data misalignment (see Vitelli, 2019, <doi:10.48550/arXiv.1912.00687>).

This package provides a set of functions for generating SPSS syntax files from the R environment.

Model age schedules of mortality, nqx, suitable for a life table. This package implements the SVD-Comp mortality model indexed by either child or child/adult mortality. Given input value(s) of either 5q0 or (5q0, 45q15), the qx() function generates single-year 1qx or 5-year 5qx conditional age-specific probabilities of dying. See Clark (2016) <doi:10.48550/arXiv.1612.01408> and Clark (2019) <doi:10.1007/s13524-019-00785-3>.

Cellular population mapping (CPM) a deconvolution algorithm in which single-cell genomics is required in only one or a few samples, where in other samples of the same tissue, only bulk genomics is measured and the underlying fine resolution cellular heterogeneity is inferred.

This package provides a Rstudio addin to download, merge and upload Rstudio settings and keymaps, essentially syncing them at will. It uses Google Drive as a cloud storage to keep the settings and keymaps files.

This package provides diagnostic tests for assessing the informativeness of survey weights in regression models. Implements difference-in-coefficients tests (Hausman 1978 <doi:10.2307/1913827>; Pfeffermann 1993 <doi:10.2307/1403631>), weight-association tests (DuMouchel and Duncan 1983 <doi:10.2307/2288185>; Pfeffermann and Sverchkov 1999 <https://www.jstor.org/stable/25051118>; Pfeffermann and Sverchkov 2003 <ISBN:9780470845672>; Wu and Fuller 2005 <https://www.jstor.org/stable/27590461>), estimating equations tests (Pfeffermann and Sverchkov 2003 <ISBN:9780470845672>), and non-parametric permutation tests. Includes simulation utilities replicating Wang et al. (2023 <doi:10.1111/insr.12509>) and extensions.

This gadget allows you to use the recipes package belonging to tidymodels to carry out the data preprocessing tasks in an interactive way. Build your recipe by dragging the variables, visually analyze your data to decide which steps to use, add those steps and preprocess your data.

Contains, as a main contribution, a function to fit a regression model with possibly right, left or interval censored observations and with the error distribution expressed as a mixture of G-splines. Core part of the computation is done in compiled C++ written using the Scythe Statistical Library Version 0.3.

Stratigraphic ranges of fossil marine animal genera from Sepkoski's (2002) published compendium. No changes have been made to any taxonomic names. However, first and last appearance intervals have been updated to be consistent with stages of the International Geological Timescale. Functionality for generating a plot of Sepkoski's evolutionary fauna is also included. For specific details on the compendium see: Sepkoski, J. J. (2002). A compendium of fossil marine animal genera. Bulletins of American Paleontology, 363, pp. 1â 560 (ISBN 0-87710-450-6). Access: <https://www.biodiversitylibrary.org/item/40634#page/5/mode/1up>.

Web application using shiny for the SSD (Species Sensitivity Distribution) module of the MOSAIC (MOdeling and StAtistical tools for ecotoxICology) platform. It estimates the Hazardous Concentration for x% of the species (HCx) from toxicity values that can be censored and provides various plotting options for a better understanding of the results. See our companion paper Kon Kam King et al. (2014) <doi:10.48550/arXiv.1311.5772>.

This comprehensive toolkit for skewed regression is designated as "SLIC" (The LIC for Distributed Skewed Regression Analysis). It is predicated on the assumption that the error term follows a skewed distribution, such as the Skew-Normal, Skew-t, or Skew-Laplace. The methodology and theoretical foundation of the package are described in Guo G.(2020) <doi:10.1080/02664763.2022.2053949>.

Collection of shiny application styling that are based on the GOV.UK Design System. See <https://design-system.service.gov.uk/components/> for details.

This package provides a shiny application estimating the operating characteristics of the Student's t-test by Student (1908) <doi:10.1093/biomet/6.1.1>, Welch's t-test by Welch (1947) <doi:10.1093/biomet/34.1-2.28>, and Wilcoxon test by Wilcoxon (1945) <doi:10.2307/3001968> in one-sample or two-sample cases, in settings defined by the user (conditional distribution, sample size per group, location parameter per group, nuisance parameter per group), using Monte Carlo simulations Malvin H. Kalos, Paula A. Whitlock (2008) <doi:10.1002/9783527626212>.

Identifying outcome relevant subgroups has now become as simple as possible! The formerly lengthy and tedious search for the needle in a haystack will be replaced by a single, comprehensive and coherent presentation. The central result of a subgroup screening is a diagram in which each single dot stands for a subgroup. The diagram may show thousands of them. The position of the dot in the diagram is determined by the sample size of the subgroup and the statistical measure of the treatment effect in that subgroup. The sample size is shown on the horizontal axis while the treatment effect is displayed on the vertical axis. Furthermore, the diagram shows the line of no effect and the overall study results. For small subgroups, which are found on the left side of the plot, larger random deviations from the mean study effect are expected, while for larger subgroups only small deviations from the study mean can be expected to be chance findings. So for a study with no conspicuous subgroup effects, the dots in the figure are expected to form a kind of funnel. Any deviations from this funnel shape hint to conspicuous subgroups.

This program calculates bioclimatic indices and fluxes (radiation, evapotranspiration, soil moisture) for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios. Predictions are based on a minimum of required inputs: latitude, precipitation, air temperature, and cloudiness. Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>.

By binding R functions and the Highcharts <http://www.highcharts.com/> charting library, sankeywheel package provides a simple way to draw dependency wheels and sankey diagrams.

Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley and Banerjee <doi:10.1016/j.envsoft.2019.104608>.

Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency.

Simple SendGrid Email API client for creating and sending emails. For more information, visit the official SendGrid Email API documentation: <https://sendgrid.com/en-us/solutions/email-api>.

This package provides tools for simulating spatially dependent predictors (continuous or binary), which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous predictors are generated using traditional multivariate normal distributions or Gauss Markov random fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288> and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors exhibiting spatial clustering can also be easily specified by the user.

Input widget that allows to construct complex filtering queries in Shiny'. It's a wrapper for JavaScript library jQuery-QueryBuilder', check <https://querybuilder.js.org/>.

This package provides a facility to generate sliced (orthogonal) Latin hypercube designs with four and five slices. For details about sliced and orthogonal Latin hypercube designs, see Yang, J. F., Lin, C. D., Qian, P. Z., and Lin, D. K. (2013). "Construction of sliced orthogonal Latin hypercube designs". Statistica Sinica, 1117-1130, <doi:10.5705/ss.2012.037>.

This package provides functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.

This package provides a new reduced-rank LDA method which works for high dimensional multi-class data.