Providing easy, portable access to NASA EarthData products through the use of bearer tokens. Much of NASA's public data catalogs hosted and maintained by its 12 Distributed Active Archive Centers ('DAACs') are now made available on the Amazon Web Services S3 storage. However, accessing this data through the standard S3 API is restricted to only to compute resources running inside us-west-2 Data Center in Portland, Oregon, which allows NASA to avoid being charged data egress rates. This package provides public access to the data from any networked device by using the EarthData login application programming interface (API), <https://www.earthdata.nasa.gov/data/earthdata-login>, providing convenient authentication and access to cloud-hosted NASA EarthData products. This makes access to a wide range of earth observation data from any location straight forward and compatible with R packages that are widely used with cloud native earth observation data (such as terra', sf', etc.).

Weather indices represent the overall weekly effect of a weather variable on crop yield throughout the cropping season. This package contains functions that can convert the weekly weather data into yearly weighted Weather indices with weights being the correlation coefficient between weekly weather data over the years and crop yield over the years. This can be done for an individual weather variable and for two weather variables at a time as the interaction effect. This method was first devised by Jain, RC, Agrawal R, and Jha, MP (1980), "Effect of climatic variables on rice yield and its forecast",MAUSAM, 31(4), 591â 596, <doi:10.54302/mausam.v31i4.3477>. Later, the method have been used by various researchers and the latest can found in Gupta, AK, Sarkar, KA, Dhakre, DS, & Bhattacharya, D (2022), "Weather Based Potato Yield Modelling using Statistical and Machine Learning Technique",Environment and Ecology, 40(3B), 1444â 1449,<https://www.environmentandecology.com/volume-40-2022>.

QuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as STRINGdb. QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using STRINGdb.

In searching for research articles, we often want to obtain lists of references from across studies, and also obtain lists of articles that cite a particular study. In systematic reviews, this supplementary search technique is known as citation chasing': forward citation chasing looks for all records citing one or more articles of known relevance; backward citation chasing looks for all records referenced in one or more articles. Traditionally, this process would be done manually, and the resulting records would need to be checked one-by-one against included studies in a review to identify potentially relevant records that should be included in a review. This package contains functions to automate this process by making use of the Lens.org API. An input article list can be used to return a list of all referenced records, and/or all citing records in the Lens.org database (consisting of PubMed, PubMed Central, CrossRef, Microsoft Academic Graph and CORE; <https://www.lens.org>).

This package provides comprehensive analytics, reporting, and testing capabilities for systematic review search strategies. The package focuses on validating search performance, generating standardized PRISMA'-compliant reports, and ensuring reproducibility in evidence synthesis. Features include precision-recall analysis, cross-database performance comparison, benchmark validation against gold standards, sensitivity analysis, temporal coverage assessment, automated report generation, and statistical comparison of search strategies. Supports multiple export formats including CSV', Excel', RIS', BibTeX', and EndNote'. Includes tools for duplicate detection, search strategy optimization, cross-validation frameworks, meta-analysis of benchmark results, power analysis for study design, and reproducibility package creation. Optionally connects to PubMed for direct database searching and real-time strategy comparison using the E-utilities API'. Enhanced with bootstrap comparison methods, McNemar test for strategy evaluation, and comprehensive visualization tools for performance assessment. Methods based on Manning et al. (2008) for information retrieval metrics, Moher et al. (2009) for PRISMA guidelines, and Sampson et al. (2006) for systematic review search methodology.

This collection of gene representation-independent functions implements the population layer of extended evolutionary and genetic algorithms and its support. The population layer consists of functions for initializing, logging, observing, evaluating a population of genes, as well as of computing the next population. For parallel evaluation of a population of genes 4 execution models - named Sequential, MultiCore, FutureApply, and Cluster - are provided. They are implemented by configuring the lapply() function. The execution model FutureApply can be externally configured as recommended by Bengtsson (2021) <doi:10.32614/RJ-2021-048>. Configurable acceptance rules and cooling schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983) <doi:10.1126/science.220.4598.671>, and Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7) offer simulated annealing or greedy randomized approximate search procedure elements. Adaptive crossover and mutation rates depending on population statistics generalize the approach of Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7).

The least-squares Monte Carlo (LSM) simulation method is a popular method for the approximation of the value of early and multiple exercise options. LSMRealOptions provides implementations of the LSM simulation method to value American option products and capital investment projects through real options analysis. LSMRealOptions values capital investment projects with cash flows dependent upon underlying state variables that are stochastically evolving, providing analysis into the timing and critical values at which investment is optimal. LSMRealOptions provides flexibility in the stochastic processes followed by underlying assets, the number of state variables, basis functions and underlying asset characteristics to allow a broad range of assets to be valued through the LSM simulation method. Real options projects are further able to be valued whilst considering construction periods, time-varying initial capital expenditures and path-dependent operational flexibility including the ability to temporarily shutdown or permanently abandon projects after initial investment has occurred. The LSM simulation method was first presented in the prolific work of Longstaff and Schwartz (2001) <doi:10.1093/rfs/14.1.113>.

Recast is state of the art navigation mesh construction toolset for games.

• It is automatic, which means that you can throw any level geometry at it and you will get robust mesh out.

• It is fast which means swift turnaround times for level designers.

The Recast process starts with constructing a voxel mold from a level geometry and then casting a navigation mesh over it. The process consists of three steps, building the voxel mold, partitioning the mold into simple regions, peeling off the regions as simple polygons.

Recast is accompanied with Detour, path-finding and spatial reasoning toolkit. You can use any navigation mesh with Detour, but of course the data generated with Recast fits perfectly.

Detour offers simple static navigation mesh which is suitable for many simple cases, as well as tiled navigation mesh which allows you to plug in and out pieces of the mesh. The tiled mesh allows you to create systems where you stream new navigation data in and out as the player progresses the level, or you may regenerate tiles as the world changes.

An implementation of a method of extending a logistic regression model beyond linear effects of the co-variates. The extension in is constructed by first equating the logistic regression model to a naive Bayes model where all the margins are specified to follow natural exponential distributions conditional on Y, that is, a model for Y given X that is specified through the distribution of X given Y, where the columns of X are assumed to be mutually independent conditional on Y. Subsequently, the model is expanded by adding vine - copulas to relax the assumption of mutual independence, where pair-copulas are added in a stage-wise, forward selection manner. Some heuristics are employed during the process of selecting edges, as well as the families of pair-copula models. After each component is added, the parameters are updated by a (smaller) number of gradient steps to maximise the likelihood. When the algorithm has stopped adding edges, based the criterion that a new edge should improve the likelihood more than k times the number new parameters, the parameters are updated with a larger number of gradient steps, or until convergence.

This package provides a comprehensive set of tools to simulate, evaluate, and compare model-assisted designs for early-phase (Phase I/II) clinical trials, including: - BOIN12 (Bayesian optimal interval phase 1/11 trial design; Lin et al. (2020) <doi:10.1200/PO.20.00257>), - BOIN-ET (Takeda, K., Taguri, M., & Morita, S. (2018) <doi:10.1002/pst.1864>), - EffTox (Thall, P. F., & Cook, J. D. (2004) <doi:10.1111/j.0006-341X.2004.00218.x>), - Ji3+3 (Joint i3+3 design; Lin, X., & Ji, Y. (2020) <doi:10.1080/10543406.2020.1818250>), - PRINTE (probability intervals of toxicity and efficacy design; Lin, X., & Ji, Y. (2021) <doi:10.1177/0962280220977009>), - STEIN (simple toxicity and efficacy interval design; Lin, R., & Yin, G. (2017) <doi:10.1002/sim.7428>), - TEPI (toxicity and efficacy probability interval design; Li, D. H., Whitmore, J. B., Guo, W., & Ji, Y. (2017) <doi:10.1158/1078-0432.CCR-16-1125>), - uTPI (utility-based toxicity Probability interval design; Shi, H., Lin, R., & Lin, X. (2024) <doi:10.1002/sim.8922>). Includes flexible simulation parameters that allow researchers to efficiently compute operating characteristics under various fixed and random trial scenarios and export the results.

An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups. Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, Elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, supervised principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9>, and supervised partial least squares analysis by Inge S. Helland <https://www.jstor.org/stable/4616159>. Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.

This package provides several confidence interval and testing procedures using event-specific win ratios for semi-competing risks data with non-terminal and terminal events, as developed in Yang et al. (2021<doi:10.1002/sim.9266>). Compared with conventional methods for survival data, these procedures are designed to utilize more data for improved inference procedures with semi-competing risks data. The event-specific win ratios were introduced in Yang and Troendle (2021<doi:10.1177/1740774520972408>). In this package, the event-specific win ratios and confidence intervals are obtained for each event type, and several testing procedures are developed for the global null of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazard assumptions, under which the event-specific win ratios converge to the hazard ratios, and a test of equal hazard ratios are provided. For summarizing the treatment effect on all events, confidence intervals for linear combinations of the event-specific win ratios are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al (2021<doi:10.1002/sim.9266>). Also, transformations are used to yield better control of the type one error rates for moderately sized data sets.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

This package contains the Multi-Species Acute Toxicity Database (CAS & SMILES columns only) [United States (US) Department of Health and Human Services (DHHS) National Institutes of Health (NIH) National Cancer Institute (NCI), "Multi-Species Acute Toxicity Database", <https://cactus.nci.nih.gov/download/acute-toxicity-db/>] combined with the Toxic Substances Control Act (TSCA) Inventory [United States Environmental Protection Agency (US EPA), "Toxic Substances Control Act (TSCA) Chemical Substance Inventory", <https://www.epa.gov/tsca-inventory/how-access-tsca-inventory

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age 5-17 questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

Identifying comorbidities, frailty, and multimorbidity in claims and administrative data is often a duplicative process. The functions contained in this package are meant to first prepare the data to a format acceptable by all other packages, then provide a uniform and simple approach to generate comorbidity and multimorbidity metrics based on these claims data. The package is ever evolving to include new metrics, and is always looking for new measures to include. The citations used in this package include the following publications: Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) <doi:10.1097/00005650-199801000-00004>, Brian J Moore, Susan White, Raynard Washington, et al. (2017) <doi:10.1097/MLR.0000000000000735>, Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) <doi:10.1016/0021-9681(87)90171-8>, Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) <doi:10.1016/0895-4356(92)90133-8>, Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>, Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) <doi:10.1093/gerona/glx229>, Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) <doi:10.1111/jgs.16310>, Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) <doi:10.15256/joc.2015.5.61>, Martin Fortin, José Almirall, and Kathryn Nicholson (2017)<doi:10.15256/joc.2017.7.122>.

The analysis of conflicting claims arises when an amount has to be divided among a set of agents with claims that exceed what is available. A rule is a way of selecting a division among the claimants. This package computes the main rules introduced in the literature from ancient times to the present. The inventory of rules covers the proportional and the adjusted proportional rules, the constrained equal awards and the constrained equal losses rules, the constrained egalitarian, the Pinilesâ and the minimal overlap rules, the random arrival and the Talmud rules. Besides, the Dominguez and Thomson and the average-of-awards rules are also included. All of them can be found in the book by W. Thomson (2019), How to divide when there isn't enough. From Aristotle, the Talmud, and Maimonides to the axiomatics of resource allocation', except for the average-of-awards rule, introduced by Mirás Calvo et al. (2022), <doi:10.1007/s00355-022-01414-6>. In addition, graphical diagrams allow the user to represent, among others, the set of awards, the paths of awards, the schedules of awards of a rule, and some indexes. A good understanding of the similarities and differences between the rules is useful for better decision-making. Therefore, this package could be helpful to students, researchers, and managers alike. For a more detailed explanation of the package, see Mirás Calvo et al. (2023), <doi:10.1016/j.dajour.2022.100160>.

Normally building a GODB is fairly complicated, involving downloading multiple database files and using these to build e.g. a mySQL database. Accessing this database is also complicated, involving an intimate knowledge of the database in order to construct reliable queries. Here we have a more modest goal, generating GOGOA3, which is a stripped down version of the GODB that was originally restricted to human genes as designated by the HUGO Gene Nomenclature Committee (HGNC) (see <https://geneontology.org/>). I have now added about two dozen additional species, namely all species represented on the Gene Ontology download page <https://current.geneontology.org/products/pages/downloads.html>. This covers most of the model organisms that are commonly used in bio-medical and basic research (assuming that anyone still has a grant to do such research). This can be built in a matter of seconds from 2 easily downloaded files (see <https://current.geneontology.org/products/pages/downloads.html> and <https://geneontology.org/docs/download-ontology/>), and it can be queried by e.g. w<-which(GOGOA3[,"HGNC"] %in% hgncList) where GOGOA3 is a matrix representing the minimalist GODB and hgncList is a list of gene identifiers. This database will be used in my upcoming package GoMiner which is based on my previous publication (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003)<doi:10.1186/gb-2003-4-4-r28>). Relevant .RData files are available from GitHub (<https://github.com/barryzee/GO/tree/main/databases>).

This package provides a reliable and flexible toolbox to score patient-reported outcome (PRO), Quality of Life (QOL), and other psychometric measures. The guiding philosophy is that scoring errors can be eliminated by using a limited number of well-tested, well-behaved functions to score PRO-like measures. The workhorse of the package is the scoreScale function, which can be used to score most single-scale measures. It can reverse code items that need to be reversed before scoring and pro-rate scores for missing item data. Currently, three different types of scores can be output: summed item scores, mean item scores, and scores scaled to range from 0 to 100. The PROscorerTools functions can be used to write new functions that score more complex measures. In fact, PROscorerTools functions are the building blocks of the scoring functions in the PROscorer package (which is a repository of functions that score specific commonly-used instruments). Users are encouraged to use PROscorerTools to write scoring functions for their favorite PRO-like instruments, and to submit these functions for inclusion in PROscorer (a tutorial vignette will be added soon). The long-term vision for the PROscorerTools and PROscorer packages is to provide an easy-to-use system to facilitate the incorporation of PRO measures into research studies in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote "best practices" for scoring and describing PRO-like measures in research.

This collection of gene representation-independent mechanisms for evolutionary and genetic algorithms contains four groups of functions: First, functions for selecting a gene in a population of genes according to its fitness value and for adaptive scaling of the fitness values as well as for performance optimization and measurement offer several variants for implementing the survival of the fittest. Second, evaluation functions for deterministic functions avoid recomputation. Evaluation of stochastic functions incrementally improve the estimation of the mean and variance of fitness values at almost no additional cost. Evaluation functions for gene repair handle error-correcting decoders. Third, timing and counting functions for profiling the algorithm pipeline are provided to assess bottlenecks in the algorithms. Fourth, a small collection of problem environments for function optimization, combinatorial optimization, and grammar-based genetic programming and grammatical evolution is provided for tutorial examples. The methods in the package are described by the following references: Baker, James E. (1987, ISBN:978-08058-0158-8), De Jong, Kenneth A. (1975) <https://deepblue.lib.umich.edu/handle/2027.42/4507>, Geyer-Schulz, Andreas (1997, ISBN:978-3-7908-0830-X), Grefenstette, John J. (1987, ISBN:978-08058-0158-8), Grefenstette, John J. and Baker, James E. (1989, ISBN:1-55860-066-3), Holland, John (1975, ISBN:0-472-08460-7), Lau, H. T. (1986) <doi:10.1007/978-3-642-61649-5>, Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) <doi:10.1007/3-540-31306-0>, Reynolds, J. C. (1993) <doi:10.1007/BF01019459>, Schaffer, J. David (1989, ISBN:1-55860-066-3), Wenstop, Fred (1980) <doi:10.1016/0165-0114(80)90031-7>, Whitley, Darrell (1989, ISBN:1-55860-066-3), Wickham, Hadley (2019, ISBN:978-815384571).

Implementation of the food safety restaurant grading system adopted by Public Health - Seattle & King County (see Ashwood, Z.C., Elias, B., and Ho. D.E. "Improving the Reliability of Food Safety Disclosure: A Quantile Adjusted Restaurant Grading System for Seattle-King County" (working paper)). As reported in the accompanying paper, this package allows jurisdictions to easily implement refinements that address common challenges with unadjusted grading systems. First, in contrast to unadjusted grading, where the most recent single routine inspection is the primary determinant of a grade, grading inputs are allowed to be flexible. For instance, it is straightforward to base the grade on average inspection scores across multiple inspection cycles. Second, the package can identify quantile cutoffs by inputting substantively meaningful regulatory thresholds (e.g., the proportion of establishments receiving sufficient violation points to warrant a return visit). Third, the quantile adjustment equalizes the proportion of establishments in a flexible number of grading categories (e.g., A/B/C) across areas (e.g., ZIP codes, inspector areas) to account for inspector differences. Fourth, the package implements a refined quantile adjustment that addresses two limitations with the stats::quantile() function when applied to inspection score datasets with large numbers of score ties. The quantile adjustment algorithm iterates over quantiles until, over all restaurants in all areas, grading proportions are within a tolerance of desired global proportions. In addition the package allows a modified definition of "quantile" from "Nearest Rank". Instead of requiring that at least p[1]% of restaurants receive the top grade and at least (p[1]+p[2])% of restaurants receive the top or second best grade for quantiles p, the algorithm searches for cutoffs so that as close as possible p[1]% of restaurants receive the top grade, and as close as possible to p[2]% of restaurants receive the second top grade.