Machine learning is widely used in information-systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, financial, or internet-security contexts, certain sub-classes are difficult to learn because they are underrepresented in training data. This R package offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm ('SANSA'), which, in contrast to other solutions, introduces a novel â placementâ parameter that can be tuned to adapt to each datasets unique manifestation of the imbalance. More information about the algorithm's parameters can be found at Nasir et al. (2022) <https://murtaza.cc/SANSA/>.

This package provides a wrapper for Blizzard's Starcraft II (a 2010 real-time strategy game) Application Programming Interface (API). All documented API calls are implemented in an easy-to-use and consistent manner.

Retrieves the most important data on parliamentary activities of the Swiss Federal Assembly via an open, machine-readable interface (see <https://ws.parlament.ch/odata.svc/>).

Identifies constant, additive, multiplicative, and user-defined simplivariate components in numeric data matrices using a genetic algorithm. Supports flexible pattern definitions and provides visualization for general biclustering applications across diverse domains. The method builds on simplivariate models as introduced in Hageman et al. (2008) <doi:10.1371/journal.pone.0003259> and is related to biclustering frameworks as reviewed by Madeira and Oliveira (2004) <doi:10.1109/TCBB.2004.2>.

Perform survival simulation with parametric survival model generated from survreg function in survival package. In each simulation coefficients are resampled from variance-covariance matrix of parameter estimates to capture uncertainty in model parameters. Prediction intervals of Kaplan-Meier estimates and hazard ratio of treatment effect can be further calculated using simulated survival data.

Fast and regularized version of GWR for large dataset, detailed in Murakami, Tsutsumida, Yoshida, Nakaya, and Lu (2019) <arXiv:1905.00266>.

Implementation of all possible forms of 2x2 and 3x3 space-filling curves, i.e., the generalized forms of the Hilbert curve <https://en.wikipedia.org/wiki/Hilbert_curve>, the Peano curve <https://en.wikipedia.org/wiki/Peano_curve> and the Peano curve in the meander type (Figure 5 in <https://eudml.org/doc/141086>). It can generates nxn curves expanded from any specific level-1 units. It also implements the H-curve and the three-dimensional Hilbert curve.

Customise Shiny disconnected screens as well as sanitize error messages to make them clearer and friendlier to the user.

Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.

This package provides tools which allow regression variables to be placed on similar scales, offering computational benefits as well as easing interpretation of regression output.

Import data from the STATcube REST API or from the open data portal of Statistics Austria. This package includes a client for API requests as well as parsing utilities for data which originates from STATcube'. Documentation about STATcubeR is provided by several vignettes included in the package as well as on the public pkgdown page at <https://statistikat.github.io/STATcubeR/>.

Run Leslie Matrix models using Monte Carlo simulations for any specified shark species. This package was developed during the publication of Smart, JJ, White, WT, Baje, L, et al. (2020) "Can multi-species shark longline fisheries be managed sustainably using size limits? Theoretically, yes. Realistically, no".J Appl Ecol. 2020; 57; 1847â 1860. <doi:10.1111/1365-2664.13659>.

Construct sketches of data via random subspace embeddings. For more details, see the following papers. Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520. Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45â 80.

The sparse vector field consensus (SparseVFC) algorithm (Ma et al., 2013 <doi:10.1016/j.patcog.2013.05.017>) for robust vector field learning. Largely translated from the Matlab functions in <https://github.com/jiayi-ma/VFC>.

Pauly et al. (2008) <http://legacy.seaaroundus.s3.amazonaws.com/doc/Researcher+Publications/dpauly/PDF/2008/Books%26Chapters/FisheriesInLargeMarineEcosystems.pdf> created (and coined the name) Stock Status Plots for a UNEP compendium on Large Marine Ecosystems(LMEs, Sherman and Hempel (2009)<https://marineinfo.org/imis?module=ref&refid=142061&printversion=1&dropIMIStitle=1>). Stock status plots are bivariate graphs summarizing the status (e.g., developing, fully exploited, overexploited, etc.), through time, of the multispecies fisheries of a fished area or ecosystem. This package contains three functions to generate stock status plots viz., SSplots_pauly() (as per the criteria proposed by Pauly et al.,2008), SSplots_kleisner() (as per the criteria proposed by Kleisner and Pauly (2011) <http://www.ecomarres.com/downloads/regional.pdf> and Kleisner et al. (2013) <doi:10.1111/j.1467-2979.2012.00469.x>)and SSplots_EPI() (as per the criteria proposed by Jayasankar et al.,2021 <https://eprints.cmfri.org.in/11364/>).

An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the caret package, other packages, or custom functions.

Includes four functions: RFactor_calc(), RFactor_est(), KFactor() and SoilLoss(). The rainfall erosivity factors can be calculated or estimated, and soil erodibility will be estimated by the equation extracted from the monograph. Soil loss will be estimated by the product of five factors (rainfall erosivity, soil erodibility, length and steepness slope, cover-management factor and support practice factor. In the future, additional functions can be included. This efforts to advance research in soil and water conservation, with fast and accurate results.

Computes segregation indices, including the Index of Dissimilarity, as well as the information-theoretic indices developed by Theil (1971) <isbn:978-0471858454>, namely the Mutual Information Index (M) and Theil's Information Index (H). The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x> and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>, is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation), and by within and between terms. The package also provides a method to decompose differences in segregation as described by Elbers (2021) <doi:10.1177/0049124121986204>. The package includes standard error estimation by bootstrapping, which also corrects for small sample bias. The package also contains functions for visualizing segregation patterns.

Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.

This package implements the Stable Balancing Weights by Zubizarreta (2015) <DOI:10.1080/01621459.2015.1023805>. These are the weights of minimum variance that approximately balance the empirical distribution of the observed covariates. For an overview, see Chattopadhyay, Hase and Zubizarreta (2020) <DOI:10.1002/sim.8659>. To solve the optimization problem in sbw', the default solver is quadprog', which is readily available through CRAN. The solver osqp is also posted on CRAN. To enhance the performance of sbw', users are encouraged to install other solvers such as gurobi and Rmosek', which require special installation. For the installation of gurobi and pogs, please follow the instructions at <https://docs.gurobi.com/projects/optimizer/en/current/reference/r.html> and <http://foges.github.io/pogs/stp/r>.

Catch advice for data-limited vertebrate and invertebrate fisheries managed by harvest slot limits using the SlotLim harvest control rule. The package accompanies the manuscript "SlotLim: catch advice for data-limited vertebrate and invertebrate fisheries managed by harvest slot limits" (Pritchard et al., in prep). Minimum data requirements: at least two consecutive years of catch data, lengthâ frequency distributions, and biomass or abundance indices (all from fishery-dependent sources); species-specific growth rate parameters (either von Bertalanffy, Gompertz, or Schnute); and either the natural mortality rate ('M') or the maximum observed age ('tmax'), from which M is estimated. The following functions have optional plotting capabilities that require ggplot2 installed: prop_target(), TBA(), SAM(), catch_advice(), catch_adjust(), and slotlim_once().

Cluster user-supplied somatic read counts with corresponding allele-specific copy number and tumor purity to infer feasible underlying intra-tumor heterogeneity in terms of number of subclones, multiplicity, and allocation (Little et al. (2019) <doi:10.1186/s13073-019-0643-9>).

This package provides a simple HTTP server allows to connect GUI clients to R.

This package provides an interface to shiny inputs used for filtering vectors, data.frames, and other objects. S7'-based implementation allows for seamless extensibility.