Flux (mass per unit time) and Load (mass) are computed from timeseries estimates of analyte concentration and discharge. Concentration timeseries are computed from regression between surrogate and user-provided analyte. Uncertainty in calculations is estimated using bootstrap resampling. Code for the processing of acoustic backscatter from horizontally profiling acoustic Doppler current profilers is provided. All methods detailed in Livsey et al (2020) <doi:10.1007/s12237-020-00734-z>, Livsey et al (2023) <doi:10.1029/2022WR033982>, and references therein.

Scelestial infers a lineage tree from single-cell DNA mutation matrix. It generates a tree with approximately maximum parsimony through a Steiner tree approximation algorithm.

Execute FOCAL (<https://en.wikipedia.org/wiki/FOCAL_(programming_language)>) source code directly in R'. This is achieved by translating FOCAL code into equivalent R commands and controlling the sequence of execution.

Collection of models and analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities and convergence, regional specialization as well as accessibility and spatial interaction models.

Graphical visualization of the birds molt to facilitate the creation of molting graph for passerines having 9 (Rmolt(data,9)) or 10 primaries (Rmolt(data,10)), and also only for the 10 first primaries (Rmolt(data,"10_0")).

Computing singular value decomposition with robustness is a challenging task. This package provides an implementation of computing robust SVD using density power divergence (<doi:10.48550/arXiv.2109.10680>). It combines the idea of robustness and efficiency in estimation based on a tuning parameter. It also provides utility functions to simulate various scenarios to compare performances of different algorithms.

This package implements the network clustering algorithm described in Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, rSpectral then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This nodeâ s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and old state, is less than the predefined tolerance. A slight variant of the fine-tuning step, which can improve speed of the calculation, is also provided. Instead of moving each node into each community in turn, we only consider moves of neighbouring nodes, found in different communities, to the community of the current node of interest. The two steps process is repeatedly applied to each new community found, subdivided each community into two new communities, until we are unable to find any division that results in a positive change in Modularity.

PADRINO houses textual representations of Integral Projection Models which can be converted from their table format into full kernels to reproduce or extend an already published analysis. Rpadrino is an R interface to this database. For more information on Integral Projection Models, see Easterling et al. (2000) <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>, and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. See Levin et al. (2021) for more information on ipmr', the engine that powers model reconstruction <doi:10.1111/2041-210X.13683>.

Recursive partitioning methods to build classification trees for ordinal responses within the CART framework. Trees are grown using the Generalized Gini impurity function, where the misclassification costs are given by the absolute or squared differences in scores assigned to the categories of the response. Pruning is based on the total misclassification rate or on the total misclassification cost.

This package provides functions for assessing variable relations and associations prior to modeling with a Random Forest algorithm (although these are relevant for any predictive model). Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) <https://digitalcommons.usu.edu/etd/8587/>.

Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting. It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) <doi:10.1016/0270-0255(86)90088-6> and has been described in detail by Ahern, Hubbard, and Galea (2009) <doi:10.1093/aje/kwp015>; Snowden, Rose, and Mortimer (2011) <doi:10.1093/aje/kwq472>; and Westreich et al. (2012) <doi:10.1002/sim.5316>.

Wraps dozens of REDCap API endpoints into a standardized R6 object. Research Electronic Data Capture ('REDCap') is a survey and database web application software maintained by Vanderbilt University. It has a robust application programming interface (API) utilized by several R packages. REDCapSync uses redcapAPI and REDCapR behind-the-scenes to retrieve all metadata, data, and log details for a project. To minimize unnecessary server calls, the interim REDCap log is analyzed and used to only update necessary records. Furthermore, the user can define custom datasets that save to a directory. Those datasets continue to refresh when projects are synced. Having a secure, standardized, API-efficient, project-agnostic R object for REDCap projects, streamlines downstream use in scripts, functions, and shiny applications.

Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488).

An R API Client for Valve's Dota2. RDota2 can be easily used to connect to the Steam API and retrieve data for Valve's popular video game Dota2. You can find out more about Dota2 at <http://store.steampowered.com/app/570/>.

Convenience functions to make some common tasks with right-to-left string printing easier, more convenient and with no need to remember long Unicode characters. Specifically helpful for right-to-left languages such as Arabic, Persian and Hebrew.

Takes matched and unmatched data and calculates Rosenbaum bounds for the treatment effect. Calculates bounds for binary outcome data, Hodges-Lehmann point estimates, Wilcoxon signed-rank test for matched data and matched IV estimators, Wilcoxon sum rank test, and for data with multiple matched controls. The sensitivity analysis methods in this package are documented in Rosenbaum (2002) Observational Studies, <doi:10.1007/978-1-4757-3692-2>, Springer-Verlag.

This package provides a collection of personal functions designed to simplify and streamline common R programming tasks. This package provides reusable tools and shortcuts for frequently used calculations and workflows.

Retrieve, map and summarize data from the VertNet.org archives (<https://vertnet.org/>). Functions allow searching by many parameters, including taxonomic names, places, and dates. In addition, there is an interface for conducting spatially delimited searches, and another for requesting large datasets via email.

This package provides an interface to Blimp software for Bayesian latent variable modeling, missing data analysis, and multiple imputation. The package generates Blimp syntax, executes Blimp models, and imports results back into R as structured objects with methods for visualization and analysis. Requires Blimp software (freely available at <https://www.appliedmissingdata.com/blimp>) to be installed separately.

Perform sigmoidal Emax model fit using Stan in a formula notation, without writing Stan model code.

This package provides methods and tools for implementing regularized multivariate functional principal component analysis ('ReMFPCA') for multivariate functional data whose variables might be observed over different dimensional domains. ReMFPCA is an object-oriented interface leveraging the extensibility and scalability of R6. It employs a parameter vector to control the smoothness of each functional variable. By incorporating smoothness constraints as penalty terms within a regularized optimization framework, ReMFPCA generates smooth multivariate functional principal components, offering a concise and interpretable representation of the data. For detailed information on the methods and techniques used in ReMFPCA', please refer to Haghbin et al. (2023) <doi:10.48550/arXiv.2306.13980>.

This package provides two general frameworks to generate a multi-layer network. This also provides several methods to reveal the embedding of both nodes and layers. The reference paper can be found from the URL mentioned below. Ting Li, Zhongyuan Lyu, Chenyu Ren, Dong Xia (2023) <arXiv:2302.04437>.

This package provides a convenient way of accessing data published by the Reserve Bank of New Zealand (RBNZ) on their website, <https://www.rbnz.govt.nz/statistics>. A range of financial and economic data is provided in spreadsheet format including exchange and interest rates, commercial lending statistics, Reserve Bank market operations, financial institution statistics, household financial data, New Zealand debt security information, and economic indicators. This package provides a method to download those spreadsheets and read them directly into R.

Determine the number of dimensions to retain in exploratory factor analysis. The main function, nest(), returns the solution and the plot(nest()) returns a plot.