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.

R bindings to SVD and eigensolvers (PROPACK, nuTRLan).

Compute centrographic statistics (central points, standard distance, standard deviation ellipse, standard deviation box) for observations taken at point locations in 2D or 3D. The sfcentral library was inspired in aspace package but conceived to be used in a spatial tidyverse context.

Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The rjags package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).

This package implements a regularization method for cumulative link models using the Smooth-Effect-on-Response Penalty (SERP). This method allows flexible modeling of ordinal data by enabling a smooth transition from a general cumulative link model to a simplified version of the same model. As the tuning parameter increases from zero to infinity, the subject-specific effects for each variable converge to a single global effect. The approach addresses common issues in cumulative link models, such as parameter unidentifiability and numerical instability, by maximizing a penalized log-likelihood instead of the standard non-penalized version. Fitting is performed using a modified Newton's method. Additionally, the package includes various model performance metrics and descriptive tools. For details on the implemented penalty method, see Ugba (2021) <doi:10.21105/joss.03705> and Ugba et al. (2021) <doi:10.3390/stats4030037>.

Secure handling of API keys can be difficult. This package provides secure convenience functions for entering / handling API keys and opening connections via inversion of control on those keys. Works seamlessly between production and developer environments.

Get programmatic access to data from the Czech public budgeting and accounting database, Státnà pokladna <https://monitor.statnipokladna.gov.cz/>.

Adds Progressive Web App support for Shiny apps, including desktop and mobile installations.

This package provides methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation. Jerald E. Pinto (â Equity Asset Valuation (4th Edition)â , 2020, ISBN: 9781119628194). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614).

This implements the Brunton et al (2016; PNAS <doi:10.1073/pnas.1517384113>) sparse identification algorithm for finding ordinary differential equations for a measured system from raw data (SINDy). The package includes a set of additional tools for working with raw data, with an emphasis on cognitive science applications (Dale and Bhat, 2018 <doi:10.1016/j.cogsys.2018.06.020>). See <https://github.com/racdale/sindyr> for examples and updates.

This package implements exact, normally approximated, and sampling-based sensitivity analysis for observational studies with contingency tables. Includes exact (kernel-based), normal approximation, and sequential importance sampling (SIS) methods using Rcpp for computational efficiency. The methods build upon the framework introduced in Rosenbaum (2002) <doi:10.1007/978-1-4757-3692-2> and the generalized design sensitivity framework developed by Chiu (2025) <doi:10.48550/arXiv.2507.17207>.

This is a compilation of my preferred themes and related theme elements for ggplot2'. I believe these themes and theme elements are aesthetically pleasing, both for pedagogical instruction and for the presentation of applied statistical research to a wide audience. These themes imply routine use of easily obtained/free fonts, simple forms of which are included in this package.

This package provides a stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.

This package provides tools for power and sample size calculation as well as design diagnostics for longitudinal mixed model settings, with a focus on stepped wedge designs. All calculations are oracle estimates i.e. assume random effect variances to be known (or guessed) in advance. The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>, extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.

Include interactive sparkline charts <http://omnipotent.net/jquery.sparkline> in all R contexts with the convenience of htmlwidgets'.

This package creates a wrapper for the SuiteSparse routines that execute the Takahashi equations. These equations compute the elements of the inverse of a sparse matrix at locations where the its Cholesky factor is structurally non-zero. The resulting matrix is known as a sparse inverse subset. Some helper functions are also implemented. Support for spam matrices is currently limited and will be implemented in the future. See Rue and Martino (2007) <doi:10.1016/j.jspi.2006.07.016> and Zammit-Mangion and Rougier (2018) <doi:10.1016/j.csda.2018.02.001> for the application of these equations to statistics.

Programmatic access to Flipside Crypto data via the Compass RPC API: <https://api-docs.flipsidecrypto.xyz/>. As simple as auto_paginate_query() but with core functions as needed for troubleshooting. Note, 0.1.1 support deprecated 2023-05-31.

Suite of helper functions for data wrangling and visualization. The only theme for these functions is that they tend towards simple, short, and narrowly-scoped. These functions are built for tasks that often recur but are not large enough in scope to warrant an ecosystem of interdependent functions.

Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. This package also provides functions for computing the classification error and classification instability of a classification procedure.

Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers. Includes implementations of methods described in Mayrhofer and Filzmoser (2023) <doi:10.1016/j.ecosta.2023.04.003>.

Fit a spatial-temporal occupancy models using a probit formulation instead of a traditional logit model.

This package provides functions for constructing mathematical models of dynamical systems from measured input-output data.

This package implements the ST-DBSCAN (spatio-temporal density-based spatial clustering of applications with noise) clustering algorithm for detecting spatially and temporally dense regions in point data, with a fast C++ backend via Rcpp'. Birant and Kut (2007) <doi:10.1016/j.datak.2006.01.013>.

An accurate and easy tool for performing linear trajectory inference on single cells using single-cell RNA sequencing data. In addition, SCORPIUS provides functions for discovering the most important genes with respect to the reconstructed trajectory, as well as nice visualisation tools. Cannoodt et al. (2016) <doi:10.1101/079509>.