Process in-situ Gamma-Ray Spectrometry for Luminescence Dating. This package allows to import, inspect and correct the energy shifts of gamma-ray spectra. It provides methods for estimating the gamma dose rate by the use of a calibration curve as described in Mercier and Falguères (2007). The package only supports Canberra CNF and TKA and Kromek SPE files.

Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.

We implement various tests for the composite hypothesis of testing the fit to the family of inverse Gaussian distributions. Included are methods presented by Allison, J.S., Betsch, S., Ebner, B., and Visagie, I.J.H. (2022) <doi:10.48550/arXiv.1910.14119>, as well as two tests from Henze and Klar (2002) <doi:10.1023/A:1022442506681>. Additionally, the package implements a test proposed by Baringhaus and Gaigall (2015) <doi:10.1016/j.jmva.2015.05.013>. For each test a parametric bootstrap procedure is implemented.

Downloads and organizes datasets using BCB's API <https://www.bcb.gov.br/>. Offers options for caching with the memoise package and , multicore/multisession with furrr and format of output data (long/wide).

Quantification, analysis, and visualization of urban greenness within city networks using data from OpenStreetMap <https://www.openstreetmap.org>.

Genie is a robust hierarchical clustering algorithm (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>). genieclust is its faster, more capable implementation (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>). It enables clustering with respect to mutual reachability distances, allowing it to act as an alternative to HDBSCAN* that can identify any number of clusters or their entire hierarchy. When combined with the deadwood package, it can act as an outlier detector. Additional package features include the Gini and Bonferroni inequality indices, external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). The Python version of genieclust is available via PyPI'.

This package provides a collection of gold price data in various currencies in the form of USD, EUR, JPY, GBP, CAD, CHF, INR, CNY, TRY, SAR, IDR, AED, THB, VND, EGP, KRW, RUB, ZAR, and AUD. This data comes from the World Gold Council. In addition, the data is in the form of daily, weekly, monthly (average and the end of period), quarterly (average and the end of period), and yearly (average and the end of period).

Computes Gregory weights for a given number nodes and function order. Anthony Ralston and Philip Rabinowitz (2001) <ISBN:9780486414546>.

Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto <https://www.tesourodireto.com.br/>.

This package provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.

Ridge regression due to Hoerl and Kennard (1970)<DOI:10.1080/00401706.1970.10488634> and generalized ridge regression due to Yang and Emura (2017)<DOI:10.1080/03610918.2016.1193195> with optimized tuning parameters. These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors. Both the ridge and generalized ridge estimators are applicable for high-dimensional regressors (p>n), where p is the number of regressors, and n is the sample size.

This package provides tools for interacting with the geographic name resolution service ('GNRS') API <https://github.com/ojalaquellueva/gnrs> and associated functionality. The GNRS is a batch application for resolving & standardizing political division names against standard name in the geonames database <http://www.geonames.org/>. The GNRS resolves political division names at three levels: country, state/province and county/parish. Resolution is performed in a series of steps, beginning with direct matching to standard names, followed by direct matching to alternate names in different languages, followed by direct matching to standard codes (such as ISO and FIPS codes). If direct matching fails, the GNRS attempts to match to standard and then alternate names using fuzzy matching, but does not perform fuzzing matching of political division codes. The GNRS works down the political division hierarchy, stopping at the current level if all matches fail. In other words, if a country cannot be matched, the GNRS does not attempt to match state or county.

This package provides a Bayesian statistical model for estimating child (under-five age group) and adult (15-60 age group) mortality. The main challenge is how to combine and integrate these different time series and how to produce unified estimates of mortality rates during a specified time span. GPR is a Bayesian statistical model for estimating child and adult mortality rates which its data likelihood is mortality rates from different data sources such as: Death Registration System, Censuses or surveys. There are also various hyper-parameters for completeness of DRS, mean, covariance functions and variances as priors. This function produces estimations and uncertainty (95% or any desirable percentiles) based on sampling and non-sampling errors due to variation in data sources. The GP model utilizes Bayesian inference to update predicted mortality rates as a posterior in Bayes rule by combining data and a prior probability distribution over parameters in mean, covariance function, and the regression model. This package uses Markov Chain Monte Carlo (MCMC) to sample from posterior probability distribution by rstan package in R. Details are given in Wang H, Dwyer-Lindgren L, Lofgren KT, et al. (2012) <doi:10.1016/S0140-6736(12)61719-X>, Wang H, Liddell CA, Coates MM, et al. (2014) <doi:10.1016/S0140-6736(14)60497-9> and Mohammadi, Parsaeian, Mehdipour et al. (2017) <doi:10.1016/S2214-109X(17)30105-5>.

When evaluating the results of a genome-wide association study (GWAS), it is important to perform a quality control to ensure that the results are valid, complete, correctly formatted, and, in case of meta-analysis, consistent with other studies that have applied the same analysis. This package was developed to facilitate and streamline this process and provide the user with a comprehensive report.

This package provides a convenient interface with the OpenAI ChatGPT API <https://openai.com/api>. gptr allows you to interact with ChatGPT', a powerful language model, for various natural language processing tasks. The gptr R package makes talking to ChatGPT in R super easy. It helps researchers and data folks by simplifying the complicated stuff, like asking questions and getting answers. With gptr', you can use ChatGPT in R without any hassle, making it simpler for everyone to do cool things with language!

An implementation of maximum simulated likelihood method for the estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>. Specifically, it allows estimating models with continuous heterogeneity such as the mixed multinomial logit and the generalized multinomial logit. It also allows estimating models with discrete heterogeneity such as the latent class and the mixed-mixed multinomial logit model.

In practical applications, the assumptions underlying generalized linear models frequently face violations, including incorrect specifications of the outcome variable's distribution or omitted predictors. These deviations can render the results of standard generalized linear models unreliable. As the sample size increases, what might initially appear as minor issues can escalate to critical concerns. To address these challenges, we adopt a permutation-based inference method tailored for generalized linear models. This approach offers robust estimations that effectively counteract the mentioned problems, and its effectiveness remains consistent regardless of the sample size.

This package provides ggplot2 geoms that allow groups of data points to be outlined or highlighted for emphasis. This is particularly useful when working with dense datasets that are prone to overplotting.

This package provides efficient geospatial thinning algorithms to reduce the density of coordinate data while maintaining spatial relationships. Implements K-D Tree and brute-force distance-based thinning, as well as grid-based and precision-based thinning methods. For more information on the methods, see Elseberg et al. (2012) <https://hdl.handle.net/10446/86202>.

This package performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2020), <arXiv:2007.08623v1>.

Perform the Blinder-Oaxaca decomposition for generalized linear model with bootstrapped standard errors. The twofold and threefold decomposition are given, even the generalized linear model output in each group.

This package provides a comprehensive toolkit for scraping and analyzing book data from <https://www.goodreads.com/>. This package provides functions to search for books, scrape book details and reviews, perform sentiment analysis on reviews, and conduct topic modeling. It's designed for researchers, data analysts, and book enthusiasts who want to gain insights from Goodreads data.

Finds adaptive strategies for sequential symmetric games using a genetic algorithm. Currently, any symmetric two by two matrix is allowed, and strategies can remember the history of an opponent's play from the previous three rounds of moves in iterated interactions between players. The genetic algorithm returns a list of adaptive strategies given payoffs, and the mean fitness of strategies in each generation.

This package contains a function called gds() which accepts three input parameters like lower limits, upper limits and the frequencies of the corresponding classes. The gds() function calculate and return the values of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', gq2', gq3'), inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate effective data analysis. For skewness and kurtosis calculations we use moments.