This package provides a comprehensive framework for estimating Generalized Process Capability Indices (GPCIs) using the Lindley approximation method for uncensored data under Bayesian inference. Evaluates point estimates and posterior expectations for classical and non-normal capability indices, including Cpy (Maiti et al., 2010), Spmk (Dey & Saha, 2019), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022), CNpmc (Alotaibi et al., 2022), CNpmkc (Saha et al., 2024), CNpk (Saha et al., 2018), and Vannman's Cp(u,v) family. Computes parametric and non-parametric bootstrap confidence intervals at 90%, 95%, and 99% levels of significance. Supports MCMC chain generation with burn-in and thinning, Highest Posterior Density (HPD) intervals, Bias, MSE, Risk values, and Heidelberger and Welch's MCMC Convergence Diagnostic with convergence probabilities. References: Lindley (1980) <doi:10.2307/2345271>, Maiti, Saha & Nanda (2010) <doi:10.1080/16843703.2010.11673233>, Saha, Dey & Maiti (2018) <doi:10.1080/21681015.2018.1437793>, Dey & Saha (2019) <doi:10.1007/s41872-019-00081-4>, Saha, Dey & Maiti (2019), Alotaibi, Dey & Saha (2022) <doi:10.1155/2022/3135264>, Saha, Dey & Nadarajah (2022) <doi:10.1080/02664763.2021.1971632>, Saha, Tripathi & Dey (2024) <doi:10.1142/S021853932450013X>.

Implementation of a two-stage framework for the joint detection-and-attribution of cross-border financial contagion. Stage one detects directional information flows between equity markets via Wavelet-Quantile Transfer Entropy, combining maximal-overlap discrete wavelet decomposition (Percival and Walden, 2000, ISBN:9780521685085) with the transfer-entropy estimator of Schreiber (2000) <doi:10.1103/PhysRevLett.85.461> and quantile conditioning following Han, Linton, Oka and Whang (2016) <doi:10.1016/j.jeconom.2016.03.001>. Stage two attributes each significant directional link to one of five mutually exclusive transmission channels (Trade, Financial, Geopolitical, Behavioural, Monetary Policy) through a multi-method structural identification architecture combining instrumental-variables two-stage least squares with channel-specific external instruments (Stock and Watson, 2018) <doi:10.1111/ecoj.12593>, LASSO-based instrument selection (Belloni, Chernozhukov and Hansen, 2014) <doi:10.1093/restud/rdt044>, local projections (Jorda, 2005) <doi:10.1257/0002828053828518>, heteroskedasticity-based identification (Rigobon, 2003) <doi:10.1162/003465303772815727>, and the Cinelli-Hazlett (2020) <doi:10.1111/rssb.12348> robustness-value sensitivity bound. Bundled datasets and replication scripts reproduce the headline findings of Bhandari, Parida and Sahu (2026) <doi:10.48550/arXiv.2604.26546>; the package is general-purpose and accommodates user-supplied returns and channel proxies.

This package provides functions for modeling, comparing, and visualizing photosynthetic light response curves using established mechanistic and empirical models like the rectangular hyperbola Michaelis-Menton based models ((eq1 (Baly (1935) <doi:10.1098/rspb.1935.0026>)) (eq2 (Kaipiainenn (2009) <doi:10.1134/S1021443709040025>)) (eq3 (Smith (1936) <doi:10.1073/pnas.22.8.504>))), hyperbolic tangent based models ((eq4 (Jassby & Platt (1976) <doi:10.4319/LO.1976.21.4.0540>)) (eq5 (Abe et al. (2009) <doi:10.1111/j.1444-2906.2008.01619.x>))), the non-rectangular hyperbola model (eq6 (Prioul & Chartier (1977) <doi:10.1093/oxfordjournals.aob.a085354>)), exponential based models ((eq8 (Webb et al. (1974) <doi:10.1007/BF00345747>)), (eq9 (Prado & de Moraes (1997) <doi:10.1007/BF02982542>))), and finally the Ye model (eq11 (Ye (2007) <doi:10.1007/s11099-007-0110-5>)). Each of these nonlinear least squares models are commonly used to express photosynthetic response under changing light conditions and has been well supported in the literature, but distinctions in each mathematical model represent moderately different assumptions about physiology and trait relationships which ultimately produce different calculated functional trait values. These models were all thoughtfully discussed and curated by Lobo et al. (2013) <doi:10.1007/s11099-013-0045-y> to express the importance of selecting an appropriate model for analysis, and methods were established in Davis et al. (in review) to evaluate the impact of analytical choice in phylogenetic analysis of the function-valued traits. Gas exchange data on 28 wild sunflower species from Davis et al.are included as an example data set here.

This package provides several confidence interval and testing procedures, based on either semiparametric (using event-specific win ratios) or nonparametric measures, including the ratio of integrated cumulative hazard (RICH) and the ratio of integrated transformed cumulative hazard (RITCH), for treatment effect inference with terminal and non-terminal events under competing risks. The semiparametric results were developed in Yang et al. (2022 <doi:10.1002/sim.9266>), and the nonparametric results were developed in Yang (2025 <doi:10.1002/sim.70205>). For comparison, results for the win ratio (Finkelstein and Schoenfeld 1999 <doi:10.1002/(SICI)1097-0258(19990615)18:11%3C1341::AID-SIM129%3E3.0.CO;2-7>), Pocock et al. 2012 <doi:10.1093/eurheartj/ehr352>, and Bebu and Lachin 2016 <doi:10.1093/biostatistics/kxv032>) are included. The package also supports univariate survival analysis with a single event. In this package, effect size estimates and confidence intervals are obtained for each event type, and several testing procedures are implemented for the global null hypothesis of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazards assumptions, under which the event-specific win ratios converge to hazard ratios, and a test of equal hazard ratios, are provided. For summarizing the treatment effect across all events, confidence intervals for linear combinations of the event-specific win ratios, RICH, or RITCH are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al. (2022 <doi:10.1002/sim.9266>) and Yang (2025 <doi:10.1002/sim.70205>).

Provides Sprockets implementation for the Rails Asset Pipeline.

Providing the container for the DockerParallel package.

Documentation at https://melpa.org/#/rope-read-mode

Image data used as examples in the loon R package.

This package enhances the ROI with the lp_solve solver.

Captures errors or missing examples encountered when iteratively running run_examplez()', and archives them.

This package provides a Minimal Example Package which demonstrates mlpack use via C++ Code from R.

This package provides a fast implementation of the greedy algorithm for the set cover problem using Rcpp'.

U-Boot is a bootloader used mostly for ARM boards. It also initializes the boards (RAM etc).

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This package enhances the R Optimization Infrastructure (ROI) package with the alabama solver for solving nonlinear optimization problems.

Yasnippets for React.

Reads data files acquired by Bruker Daltonics matrix-assisted laser desorption/ionization-time-of-flight mass spectrometer of the *flex series.

Facilitates mapping by making natural earth map data from http:// www.naturalearthdata.com/ more easily available to R users. Focuses on vector data.

The rocprofiler-register library coordinates the modification of the intercept API table(s) of the HSA/HIP/ROCTx runtime libraries by the ROCprofiler (v2) library.

Standard model for Recola2.

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

This is a collection of tools to allow the medical professional to calculate appropriate reference ranges (intervals) with confidence intervals around the limits for diagnostic purposes.

Build regular expressions using grammar and functionality inspired by <https://github.com/VerbalExpressions>. Usage of the %>% is encouraged to build expressions in a chain-like fashion.