This package performs goodness of fits tests for both high and low-dimensional linear models. It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity. In addition one can test the significance of potentially large groups of variables, and also produce p-values for the significance of individual variables in high-dimensional linear regression.

This package provides tools are provided for estimating, testing, and simulating abundance in a two-event (Petersen) mark-recapture experiment. Functions are given to calculate the Petersen, Chapman, and Bailey estimators and associated variances. However, the principal utility is a set of functions to simulate random draws from these estimators, and use these to conduct hypothesis tests and power calculations. Additionally, a set of functions are provided for generating confidence intervals via bootstrapping. Functions are also provided to test abundance estimator consistency under complete or partial stratification, and to calculate stratified or partially stratified estimators. Functions are also provided to calculate recommended sample sizes. Referenced methods can be found in Arnason et al. (1996) <ISSN:0706-6457>, Bailey (1951) <DOI:10.2307/2332575>, Bailey (1952) <DOI:10.2307/1913>, Chapman (1951) NAID:20001644490, Cohen (1988) ISBN:0-12-179060-6, Darroch (1961) <DOI:10.2307/2332748>, and Robson and Regier (1964) <ISSN:1548-8659>.

Compress local and online images using the reSmush.it API service <https://resmush.it/>.

Integrates population dynamics and dispersal into a mechanistic virtual species simulator. The package can be used to study the effects of environmental change on population growth and range shifts. It allows for simple and straightforward definition of population dynamics (including positive density dependence), extensive possibilities for defining dispersal kernels, and the ability to generate virtual ecologist data. Learn more about the rangr at <https://docs.ropensci.org/rangr/>.

Automatically creates separate regression models for different spatial regions. The prediction surface is smoothed using a regional border smoothing method. If regional models are continuous, the resulting prediction surface is continuous across the spatial dimensions, even at region borders. Methodology is described in Wagstaff and Bean (2023) <doi:10.32614/RJ-2023-004>.

Visualize your favorite XKCD comic strip directly from R. XKCD <https://xkcd.com> web comic content is provided under the Creative Commons Attribution-NonCommercial 2.5 License.

This package provides a tool to exchange data between R and Raven sound analysis software (Cornell Lab of Ornithology). Functions work on data formats compatible with the R package warbleR'.

This package provides a Pure R implementation of Bayesian Global Optimization with Gaussian Processes.

This package provides a tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.

This package provides the Book-Crossing Dataset for the package recommenderlab.

This package provides easy access to The Reptile Database', a comprehensive catalogue of all living reptile species and their classification. This package includes taxonomic data for over 10,000 reptile species, approximately 2,800 of which are subspecies, covering all extant reptiles. The dataset features taxonomic names, synonyms, distribution data, type specimens, and literature references, making it ready for research and analysis. Data is sourced from The Reptile Database <http://www.reptile-database.org/>.

Exploit controlled vocabularies organized on tematres servers.

Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in remverse and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>).

The R Formatter formats R source code. It is very much based on formatR, but tries to improve it by heuristics. For example, spaces can be forced around the division operator "/".

Much as roxygen2 allows one to document functions in the same file as the function itself, roxut allows one to write the unit tests in the same file as the function. Once processed, the unit tests are moved to the appropriate directory. Currently supports testthat and tinytest frameworks. The roxygen2 package provides much of the infrastructure.

Range Modeling Metadata Standards (RMMS) address three challenges: they (i) are designed for convenience to encourage use, (ii) accommodate a wide variety of applications, and (iii) are extensible to allow the community of range modelers to steer it as needed. RMMS are based on a data dictionary that specifies a hierarchical structure to catalog different aspects of the range modeling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to provide their own values. Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in more detail. Note that users who prefer to use the R package ecospat can obtain it from <https://github.com/ecospat/ecospat>.

Draw maps using the javascript library roughjs'. This allows to draw sketchy, hand-drawn-like maps.

Get data from Linkedin Advertising API <https://learn.microsoft.com/en-us/linkedin/marketing/overview?view=li-lms-2023-10>. You can load ad account hierarchy (accounts, users, campaign groups, campaigns and creatives) and also you can load ad analytics data from your Linkedin Ad account.

This package provides functions for phylogenetic analysis (Castiglione et al., 2018 <doi:10.1111/2041-210X.12954>). The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, the computation of ontogenetic shape vectors and test for morphological convergence.

This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.

This package provides a GUI for the orloca package is provided as a Rcmdr plug-in. The package deals with continuos planar location problems.

Converts data to STL (stereolithography) files that can be used to feed a 3-dimensional printer. The 3-dimensional output from a function can be materialized into a solid surface in a plastic material, therefore allowing more detailed examination. There are many possible uses for this new tool, such as to examine mathematical expressions with very irregular shapes, to aid teaching people with impaired vision, to create raised relief maps from digital elevation maps (DEMs), to bridge the gap between mathematical tools and rapid prototyping, and many more. Ian Walker created the function r2stl() and Jose Gama assembled the package.

Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.

Enhances the R Optimization Infrastructure ('ROI') package with the NLopt solver for solving nonlinear optimization problems.