R package accompanying the book Working with dynamic models for agriculture and environment, by Daniel Wallach (INRAE), David Makowski (INRAE), James W. Jones (U.of Florida), Francois Brun (ACTA), 2019.

This package provides tools for estimating Zero-Inflated INAR(1) (ZI-INAR(1)) and Hurdle INAR(1) (H-INAR(1)) models using Stan'. It allows users to simulate time series data for these models, estimate parameters, and evaluate model fit using various criteria. Functions include model estimation, simulation, and likelihood-based metrics.

Generates Realizations of First-Order Integer Valued Autoregressive Processes with Zero-Inflated Innovations (ZINAR(1)) and Estimates its Parameters as described in Garay et al. (2021) <doi:10.1007/978-3-030-82110-4_2>.

We provide a flexible Zero-inflated Poisson-Gamma Model (ZIPG) by connecting both the mean abundance and the variability to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. These functions can be used to analyze microbiome count data with zero-inflation and overdispersion. The model is discussed in Jiang et al (2023) <doi:10.1080/01621459.2022.2151447>.

Fits Dirichlet regression and zero-and-one inflated Dirichlet regression with Bayesian methods implemented in Stan. These models are sometimes referred to as trinomial mixture models; covariates and overdispersion can optionally be included.

This package provides quantitative tools for assessing zoonotic disease risk across animal, human, environmental, and transmission interfaces. The package supports exposure and transmission risk estimation, spillover risk assessment, risk scoring, cross-species comparison, transmission-network analysis, Monte Carlo uncertainty simulation, and sensitivity analysis. The One Health framework underlying these assessments is described by World Health Organization, Food and Agriculture Organization of the United Nations, United Nations Environment Programme, and World Organisation for Animal Health (2022) <doi:10.4060/cc2289en>.

The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) <doi: 10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi: 10.1016/j.cell.2017.06.010>) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of ZetaSuite in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) <doi: 10.1038/s41586-018-0698-6>). In ZetaSuite', we have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength.

Implementation of new statistical distributions in (0, 1) interval. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters using Generalized Additive Models for Location, Scale and Shape, GAMLSS by Rigby & Stasinopoulos (2005) <doi:10.1111/j.1467-9876.2005.00510.x>.

Interface to the Zotero reference manager <https://www.zotero.org> through its public web API and local client database. Provides paginated reads, versioned writes, read-only local queries, batch plans and resumable ledgers, bibliographic comparison, metadata mapping, file attachment, and optional semantic-index queries.

Make working with ZIP codes in R painless with an integrated dataset of U.S. ZIP codes and functions for working with them. Search ZIP codes by multiple geographies, including state, county, city & across time zones. Also included are functions for relating ZIP codes to Census data, geocoding & distance calculations. New analyses can select an immutable modern data bundle through the next-generation API, while the historical interface remains compatible with version 0.3.5 for reproducible research.

Uses bootstrap to test zero order correlation being equal to a partial or semi-partial correlation (one or two tailed). Confidence intervals for the parameter (zero order minus partial) can also be determined. Implements the bias-corrected and accelerated bootstrap method as described in "An Introduction to the Bootstrap" Efron (1983) <0-412-04231-2>.

Parses real-world HTML with a bundled copy of the Gumbo parser (<https://codeberg.org/gumbo-parser/gumbo-parser>), which follows the WHATWG parsing algorithm, so that no system library is required. Documents become immutable trees navigated with a documented subset of CSS selectors. Attributes, text, lists, tables, links, forms and page metadata ('JSON-LD', microdata) are extracted into ordinary character vectors, lists and data frames, and nodes convert to Markdown'. Input is a string, raw bytes, a file, a URL or a connection, and raw input is decoded as browsers decode it, from a byte-order mark or a <meta> declaration. Parsing is bounded by limits on input size, native memory and nesting depth.

Implementation of four extensions of the Zipf distribution: the Marshall-Olkin Extended Zipf (MOEZipf) Pérez-Casany, M., & Casellas, A. (2013) <arXiv:1304.4540>, the Zipf-Poisson Extreme (Zipf-PE), the Zipf-Poisson Stopped Sum (Zipf-PSS) and the Zipf-Polylog distributions. In log-log scale, the two first extensions allow for top-concavity and top-convexity while the third one only allows for top-concavity. All the extensions maintain the linearity associated with the Zipf model in the tail.

Facilitates making a connection to the Zendesk API and executing various queries. You can use it to get ticket, ticket metrics, and user data. The Zendesk documentation is available at <https://developer.zendesk.com/rest_api /docs/support/introduction>. This package is not supported by Zendesk (owner of the software).

Fits Bayesian zero-inflated negative binomial regression models with Gaussian process random effects for spatial, temporal, or spatiotemporal count data. Provides Markov chain Monte Carlo sampling, configurable random effects in the zero-inflation and count components, and posterior predictive draws. Implements a full GP version of the methods described by He and Huang (2024) <doi:10.1016/j.jspi.2023.106098>.