This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).

Implementation of S4 class of sets and multisets of numbers. The implementation is based on the hash table from the package hash'. Quick operations are allowed when the set is a dynamic object. The implementation is discussed in detail in Ceoldo and Wit (2023) <arXiv:2304.09809>.

This package provides a function to calculate infinite-jackknife-based standard errors for fixed effects parameters in brms models, handling both clustered and independent data. References: Ji et al. (2024) <doi:10.48550/arXiv.2407.09772>; Giordano et al. (2024) <doi:10.48550/arXiv.2305.06466>.

The multivariate regression association measure quantifies the predictability of one random vector from another. This package provides a function for estimating and performing inference on this measure. A variable selection algorithm based on this measure is also included. For more details, see Shih and Chen (2025) <in revision>.

Perform multi-trait rare-variant association tests using the summary statistics and adjust for possible sample overlap. Package is based on "Multi-Trait Analysis of Rare-Variant Association Summary Statistics using MTAR" by Luo, L., Shen, J., Zhang, H., Chhibber, A. Mehrotra, D.V., Tang, Z., 2019 (submitted).

To perform main effect matrix factor model (MEFM) estimation for a given matrix time series as described in Lam and Cen (2024) <doi:10.48550/arXiv.2406.00128>. Estimation of traditional matrix factor models is also supported. Supplementary functions for testing MEFM over factor models are included.

Subsampling based variable selection for low dimensional generalized linear models. The methods repeatedly subsample the data minimizing an information criterion (AIC/BIC) over a sequence of nested models for each subsample. Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models.

In linear LS regression, calculate for a given design matrix the multiplier K of coefficient standard errors such that the confidence intervals [b - K*SE(b), b + K*SE(b)] have a guaranteed coverage probability for all coefficient estimates b in any submodels after performing arbitrary model selection.

Publish data sets, models, and other R objects, making it easy to share them across projects and with your colleagues. You can pin objects to a variety of "boards", including local folders (to share on a networked drive or with DropBox'), Posit Connect', AWS S3', and more.

Fit quantile regression neural network models with optional left censoring, partial monotonicity constraints, generalized additive model constraints, and the ability to fit multiple non-crossing quantile functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005> and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.

This package implements L0-constrained Multi-Task Learning and domain generalization algorithms. The algorithms are coded in Julia allowing for fast implementations of the coordinate descent and local combinatorial search algorithms. For more details, see a preprint of the paper: Loewinger et al., (2022) <arXiv:2212.08697>.

Covers k-table control analysis using multivariate control charts for qualitative variables using fundamentals of multiple correspondence analysis and multiple factor analysis. The graphs can be shown in a flat or interactive way, in the same way all the outputs can be shown in an interactive shiny panel.

This package provides methods for faster extraction (about 5x faster in a few test cases) of variance-covariance matrices and standard errors from models. Methods in the stats package tend to rely on the summary method, which may waste time computing other summary statistics which are summarily ignored.

Lossless webp images are 26% smaller in size compared to PNG. Lossy webp images are 25-34% smaller in size compared to JPEG. This package reads and writes webp images into a 3 (rgb) or 4 (rgba) channel bitmap array using conventions from the jpeg and png packages.

MIME types are shorthand descriptors for file contents and can be determined from "magic" bytes in file headers, file contents or intuited from file extensions. Tools are provided to perform curated "magic" tests as well as mapping MIME types from a database of over 1,500 extension mappings.

This is a package for analysis of case-control data in genetic epidemiology. It provides a set of statistical methods for evaluating gene-environment (or gene-genes) interactions under multiplicative and additive risk models, with or without assuming gene-environment (or gene-gene) independence in the underlying population.

GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface.

This package lets you estimate fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and compute average partial effects. Incidental parameter bias can be reduced with an asymptotic bias correction proposed by Fernandez-Val (2009) <doi:10.1016/j.jeconom.2009.02.007>.

This package provides a set of tools to perform Quantitative Trait Locus (QTL) analysis in experimental crosses. It is a reimplementation of the R/qtl package to better handle high-dimensional data and complex cross designs. Broman et al. (2018) <doi:10.1534/genetics.118.301595>.

Redeal is a deal generator written in Python. It outputs deals satisfying whatever conditions you specify --- deals with a double void, deals with a strong 2♣ opener opposite a yarborough, etc. Using Bo Haglund's double dummy solver, it can even solve the hands it has generated for you.

Constructs various robust quality control charts based on the median or Hodges-Lehmann estimator (location) and the median absolute deviation (MAD) or Shamos estimator (scale). The estimators used for the robust control charts are all unbiased with a sample of finite size. For more details, see Park, Kim and Wang (2022) <doi:10.1080/03610918.2019.1699114>. In addition, using this R package, the conventional quality control charts such as X-bar, S, R, p, np, u, c, g, h, and t charts are also easily constructed. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C1091319).

Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) <doi:10.1109/ICPR.1992.201708>, including popular variants like extreme learning machines, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, sparse RWNN, Zhang et al. (2019) <doi:10.1016/j.neunet.2019.01.007>, and deep RWNN, Henrà quez et al. (2018) <doi:10.1109/IJCNN.2018.8489703>. It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) <doi:10.1109/ECCE47101.2021.9595113>, boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) <doi:10.1016/j.patcog.2021.107978>.

This package contains implementations of recurrent event data analysis routines including (1) survival and recurrent event data simulation from stochastic process point of view by the thinning method proposed by Lewis and Shedler (1979) <doi:10.1002/nav.3800260304> and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976), (2) the mean cumulative function (MCF) estimation by the Nelson-Aalen estimator of the cumulative hazard rate function, (3) two-sample recurrent event responses comparison with the pseudo-score tests proposed by Lawless and Nadeau (1995) <doi:10.2307/1269617>, (4) gamma frailty model with spline rate function following Fu, et al. (2016) <doi:10.1080/10543406.2014.992524>.

Recursive algorithms for computing various relatedness coefficients, including pairwise kinship, kappa and identity coefficients. Both autosomal and X-linked coefficients are computed. Founders are allowed to be inbred, which enables construction of any given kappa coefficients, as described in Vigeland (2020) <doi:10.1007/s00285-020-01505-x>. In addition to the standard coefficients, ribd also computes a range of lesser-known coefficients, including generalised kinship coefficients, multi-person coefficients and two-locus coefficients (Vigeland, 2023, <doi:10.1093/g3journal/jkac326>). Many features of ribd are available through the online app QuickPed at <https://magnusdv.shinyapps.io/quickped>; see Vigeland (2022) <doi:10.1186/s12859-022-04759-y>.