There are diverse purposes such as biomarker confirmation, novel biomarker discovery, constructing predictive models, model-based prediction, and validation. It handles binary, continuous, and time-to-event outcomes at the sample or patient level. - Biomarker confirmation utilizes established functions like glm() from stats', coxph() from survival', surv_fit(), and ggsurvplot() from survminer'. - Biomarker discovery and variable selection are facilitated by three LASSO-related functions LASSO2(), LASSO_plus(), and LASSO2plus(), leveraging the glmnet R package with additional steps. - Eight versatile modeling functions are offered, each designed for predictive models across various outcomes and data types. 1) LASSO2(), LASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable selection using LASSO methods and construct predictive models based on selected variables. 2) XGBtraining() employs XGBoost for model building and is the only function not involving variable selection. 3) Functions like LASSO2_XGBtraining(), LASSOplus_XGBtraining(), and LASSO2plus_XGBtraining() combine LASSO-related variable selection with XGBoost for model construction. - All models support prediction and validation, requiring a testing dataset comparable to the training dataset. Additionally, the package introduces XGpred() for risk prediction based on survival data, with the XGpred_predict() function available for predicting risk groups in new datasets. The methodology is based on our new algorithms and various references: - Hastie et al. (1992, ISBN 0 534 16765-9), - Therneau et al. (2000, ISBN 0-387-98784-3), - Kassambara et al. (2021) <https://CRAN.R-project.org/package=survminer>, - Friedman et al. (2010) <doi:10.18637/jss.v033.i01>, - Simon et al. (2011) <doi:10.18637/jss.v039.i05>, - Harrell (2023) <https://CRAN.R-project.org/package=rms>, - Harrell (2023) <https://CRAN.R-project.org/package=Hmisc>, - Chen and Guestrin (2016) <doi:10.48550/arXiv.1603.02754>, - Aoki et al. (2023) <doi:10.1200/JCO.23.01115>.

First, we provide functions to calculate the partial derivative of the first-passage time diffusion probability density function (PDF) and cumulative distribution function (CDF) with respect to the first-passage time t (only for PDF), the upper barrier a, the drift rate v, the relative starting point w, the non-decision time t0, the inter-trial variability of the drift rate sv, the inter-trial variability of the rel. starting point sw, and the inter-trial variability of the non-decision time st0. In addition the PDF and CDF themselves are also provided. Most calculations are done on the logarithmic scale to make it more stable. Since the PDF, CDF, and their derivatives are represented as infinite series, we give the user the option to control the approximation errors with the argument precision'. For the numerical integration we used the C library cubature by Johnson, S. G. (2005-2013) <https://github.com/stevengj/cubature>. Numerical integration is required whenever sv, sw, and/or st0 is not zero. Note that numerical integration reduces speed of the computation and the precision cannot be guaranteed anymore. Therefore, whenever numerical integration is used an estimate of the approximation error is provided in the output list. Note: The large number of contributors (ctb) is due to copying a lot of C/C++ code chunks from the GNU Scientific Library (GSL). Second, we provide methods to sample from the first-passage time distribution with or without user-defined truncation from above. The first method is a new adaptive rejection sampler building on the works of Gilks and Wild (1992; <doi:10.2307/2347565>) and Hartmann and Klauer (in press). The second method is a rejection sampler provided by Drugowitsch (2016; <doi:10.1038/srep20490>). The third method is an inverse transformation sampler. The fourth method is a "pseudo" adaptive rejection sampler that builds on the first method. For more details see the corresponding help files.

Self-reported health, happiness, attitudes, and other statuses or perceptions are often the subject of biases that may come from different sources. For example, the evaluation of an individualâ s own health may depend on previous medical diagnoses, functional status, and symptoms and signs of illness; as on well as life-style behaviors, including contextual social, gender, age-specific, linguistic and other cultural factors (Jylha 2009 <doi:10.1016/j.socscimed.2009.05.013>; Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). The hopit package offers versatile functions for analyzing different self-reported ordinal variables, and for helping to estimate their biases. Specifically, the package provides the function to fit a generalized ordered probit model that regresses original self-reported status measures on two sets of independent variables (King et al. 2004 <doi:10.1017/S0003055403000881>; Jurges 2007 <doi:10.1002/hec.1134>; Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). The first set of variables (e.g., health variables) included in the regression are individual statuses and characteristics that are directly related to the self-reported variable. In the case of self-reported health, these could be chronic conditions, mobility level, difficulties with daily activities, performance on grip strength tests, anthropometric measures, and lifestyle behaviors. The second set of independent variables (threshold variables) is used to model cut-points between adjacent self-reported response categories as functions of individual characteristics, such as gender, age group, education, and country (Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). The model helps to adjust for specific socio-demographic and cultural differences in how the continuous latent health is projected onto the ordinal self-rated measure. The fitted model can be used to calculate an individual predicted latent status variable, a latent index, and standardized latent coefficients; and makes it possible to reclassify a categorical status measure that has been adjusted for inter-individual differences in reporting behavior.

Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially those of biomacromolecules such as proteins. Biological Magnetic Resonance Data Bank ('BMRB') is a repository for Data from NMR Spectroscopy on Proteins, Peptides, Nucleic Acids, and other Biomolecules. Currently, BMRB offers an R package RBMRB to fetch data, however, it doesn't easily offer individual data file downloading and storing in a local directory. When using RBMRB', the data will stored as an R object, which fundamentally hinders the NMR researches to access the rich information from raw data, for example, the metadata. Here, BMRBr File Downloader ('BMRBr') offers a more fundamental, low level downloader, which will download original deposited .str format file. This type of file contains information such as entry title, authors, citation, protein sequences, and so on. Many factors affect NMR experiment outputs, such as temperature, resonance sensitivity and etc., approximately 40% of the entries in the BMRB have chemical shift accuracy problems [1,2] Unfortunately, current reference correction methods are heavily dependent on the availability of assigned protein chemical shifts or protein structure. This is my current research project is going to solve, which will be included in the future release of the package. The current version of the package is sufficient and robust enough for downloading individual BMRB data file from the BMRB database <http://www.bmrb.wisc.edu>. The functionalities of this package includes but not limited: * To simplifies NMR researches by combine data downloading and results analysis together. * To allows NMR data reaches a broader audience that could utilize more than just chemical shifts but also metadata. * To offer reference corrected data for entries without assignment or structure information (future release). Reference: [1] E.L. Ulrich, H. Akutsu, J.F. Doreleijers, Y. Harano, Y.E. Ioannidis, J. Lin, et al., BioMagResBank, Nucl. Acids Res. 36 (2008) D402â 8. <doi:10.1093/nar/gkm957>. [2] L. Wang, H.R. Eghbalnia, A. Bahrami, J.L. Markley, Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications, J. Biomol. NMR. 32 (2005) 13â 22. <doi:10.1007/s10858-005-1717-0>.

This method is a new class of model selection strategies, for mixed model selection, which includes linear and generalized linear mixed models. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from among those within the fence according to a criterion which can be made flexible. References: 1. Jiang J., Rao J.S., Gu Z., Nguyen T. (2008), Fence Methods for Mixed Model Selection. The Annals of Statistics, 36(4): 1669-1692. <DOI:10.1214/07-AOS517> <https://projecteuclid.org/euclid.aos/1216237296>. 2. Jiang J., Nguyen T., Rao J.S. (2009), A Simplified Adaptive Fence Procedure. Statistics and Probability Letters, 79, 625-629. <DOI:10.1016/j.spl.2008.10.014> <https://www.researchgate.net/publication/23991417_A_simplified_adaptive_fence_procedure> 3. Jiang J., Nguyen T., Rao J.S. (2010), Fence Method for Nonparametric Small Area Estimation. Survey Methodology, 36(1), 3-11. <http://publications.gc.ca/collections/collection_2010/statcan/12-001-X/12-001-x2010001-eng.pdf>. 4. Jiming Jiang, Thuan Nguyen and J. Sunil Rao (2011), Invisible fence methods and the identification of differentially expressed gene sets. Statistics and Its Interface, Volume 4, 403-415. <http://www.intlpress.com/site/pub/files/_fulltext/journals/sii/2011/0004/0003/SII-2011-0004-0003-a014.pdf>. 5. Thuan Nguyen & Jiming Jiang (2012), Restricted fence method for covariate selection in longitudinal data analysis. Biostatistics, 13(2), 303-314. <DOI:10.1093/biostatistics/kxr046> <https://academic.oup.com/biostatistics/article/13/2/303/263903/Restricted-fence-method-for-covariate-selection-in>. 6. Thuan Nguyen, Jie Peng, Jiming Jiang (2014), Fence Methods for Backcross Experiments. Statistical Computation and Simulation, 84(3), 644-662. <DOI:10.1080/00949655.2012.721885> <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3891925/>. 7. Jiang, J. (2014), The fence methods, in Advances in Statistics, Hindawi Publishing Corp., Cairo. <DOI:10.1155/2014/830821>. 8. Jiming Jiang and Thuan Nguyen (2015), The Fence Methods, World Scientific, Singapore. <https://www.abebooks.com/9789814596060/Fence-Methods-Jiming-Jiang-981459606X/plp>.

We provide a collection of various classical tests and latest normal-reference tests for comparing high-dimensional mean vectors including two-sample and general linear hypothesis testing (GLHT) problem. Some existing tests for two-sample problem [see Bai, Zhidong, and Hewa Saranadasa.(1996) <https://www.jstor.org/stable/24306018>; Chen, Song Xi, and Ying-Li Qin.(2010) <doi:10.1214/09-aos716>; Srivastava, Muni S., and Meng Du.(2008) <doi:10.1016/j.jmva.2006.11.002>; Srivastava, Muni S., Shota Katayama, and Yutaka Kano.(2013)<doi:10.1016/j.jmva.2012.08.014>]. Normal-reference tests for two-sample problem [see Zhang, Jin-Ting, Jia Guo, Bu Zhou, and Ming-Yen Cheng.(2020) <doi:10.1080/01621459.2019.1604366>; Zhang, Jin-Ting, Bu Zhou, Jia Guo, and Tianming Zhu.(2021) <doi:10.1016/j.jspi.2020.11.008>; Zhang, Liang, Tianming Zhu, and Jin-Ting Zhang.(2020) <doi:10.1016/j.ecosta.2019.12.002>; Zhang, Liang, Tianming Zhu, and Jin-Ting Zhang.(2023) <doi:10.1080/02664763.2020.1834516>; Zhang, Jin-Ting, and Tianming Zhu.(2022) <doi:10.1080/10485252.2021.2015768>; Zhang, Jin-Ting, and Tianming Zhu.(2022) <doi:10.1007/s42519-021-00232-w>; Zhu, Tianming, Pengfei Wang, and Jin-Ting Zhang.(2023) <doi:10.1007/s00180-023-01433-6>]. Some existing tests for GLHT problem [see Fujikoshi, Yasunori, Tetsuto Himeno, and Hirofumi Wakaki.(2004) <doi:10.14490/jjss.34.19>; Srivastava, Muni S., and Yasunori Fujikoshi.(2006) <doi:10.1016/j.jmva.2005.08.010>; Yamada, Takayuki, and Muni S. Srivastava.(2012) <doi:10.1080/03610926.2011.581786>; Schott, James R.(2007) <doi:10.1016/j.jmva.2006.11.007>; Zhou, Bu, Jia Guo, and Jin-Ting Zhang.(2017) <doi:10.1016/j.jspi.2017.03.005>]. Normal-reference tests for GLHT problem [see Zhang, Jin-Ting, Jia Guo, and Bu Zhou.(2017) <doi:10.1016/j.jmva.2017.01.002>; Zhang, Jin-Ting, Bu Zhou, and Jia Guo.(2022) <doi:10.1016/j.jmva.2021.104816>; Zhu, Tianming, Liang Zhang, and Jin-Ting Zhang.(2022) <doi:10.5705/ss.202020.0362>; Zhu, Tianming, and Jin-Ting Zhang.(2022) <doi:10.1007/s00180-021-01110-6>; Zhang, Jin-Ting, and Tianming Zhu.(2022) <doi:10.1016/j.csda.2021.107385>].

Companion package, functions, data sets, examples for the book Patrice Bertail and Anna Dudek (2025), Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted. Kreiss, J.-P. and Paparoditis, E. (2003) <doi:10.1214/aos/1074290332> Politis, D.N., and White, H. (2004) <doi:10.1081/ETC-120028836> Patton, A., Politis, D.N., and White, H. (2009) <doi:10.1080/07474930802459016> Tsybakov, A. B. (2018) <doi:10.1007/b13794> Bickel, P., and Sakov, A. (2008) <doi:10.1214/18-AOS1803> Götze, F. and RaÄ kauskas, A. (2001) <doi:10.1214/lnms/1215090074> Politis, D. N., Romano, J. P., & Wolf, M. (1999, ISBN:978-0-387-98854-2) Carlstein E. (1986) <doi:10.1214/aos/1176350057> Künsch, H. (1989) <doi:10.1214/aos/1176347265> Liu, R. and Singh, K. (1992) <https://www.stat.purdue.edu/docs/research/tech-reports/1991/tr91-07.pdf> Politis, D.N. and Romano, J.P. (1994) <doi:10.1080/01621459.1994.10476870> Politis, D.N. and Romano, J.P. (1992) <https://www.stat.purdue.edu/docs/research/tech-reports/1991/tr91-07.pdf> Patrice Bertail, Anna E. Dudek. (2022) <doi:10.3150/23-BEJ1683> Dudek, A.E., LeÅ kow, J., Paparoditis, E. and Politis, D. (2014a) <https://ideas.repec.org/a/bla/jtsera/v35y2014i2p89-114.html> Beran, R. (1997) <doi:10.1023/A:1003114420352> B. Efron, and Tibshirani, R. (1993, ISBN:9780429246593) Bickel, P. J., Götze, F. and van Zwet, W. R. (1997) <doi:10.1007/978-1-4614-1314-1_17> A. C. Davison, D. Hinkley (1997) <doi:10.2307/1271471> Falk, M., & Reiss, R. D. (1989) <doi:10.1007/BF00354758> Lahiri, S. N. (2003) <doi:10.1007/978-1-4757-3803-2> Shimizu, K. .(2017) <doi:10.1007/978-3-8348-9778-7> Park, J.Y. (2003) <doi:10.1111/1468-0262.00471> Kirch, C. and Politis, D. N. (2011) <doi:10.48550/arXiv.1211.4732> Bertail, P. and Dudek, A.E. (2024) <doi:10.3150/23-BEJ1683> Dudek, A. E. (2015) <doi:10.1007/s00184-014-0505-9> Dudek, A. E. (2018) <doi:10.1080/10485252.2017.1404060> Bertail, P., Clémençon, S. (2006a) <https://ideas.repec.org/p/crs/wpaper/2004-47.html> Bertail, P. and Clémençon, S. (2006, ISBN:978-0-387-36062-1) RaduloviÄ , D. (2006) <doi:10.1007/BF02603005> Bertail, P. Politis, D. N. Rhomari, N. (2000) <doi:10.1080/02331880008802701> Nordman, D.J. Lahiri, S.N.(2004) <doi:10.1214/009053604000000779> Politis, D.N. Romano, J.P. (1993) <doi:10.1006/jmva.1993.1085> Hurvich, C. M. and Zeger, S. L. (1987, ISBN:978-1-4612-0099-4) Bertail, P. and Dudek, A. (2021) <doi:10.1214/20-EJS1787> Bertail, P., Clémençon, S. and Tressou, J. (2015) <doi:10.1111/jtsa.12105> Asmussen, S. (1987) <doi:10.1007/978-3-662-11657-9> Efron, B. (1979) <doi:10.1214/aos/1176344552> Gray, H., Schucany, W. and Watkins, T. (1972) <doi:10.2307/2335521> Quenouille, M.H. (1949) <doi:10.1111/j.2517-6161.1949.tb00023.x> Quenouille, M. H. (1956) <doi:10.2307/2332914> Prakasa Rao, B. L. S. and Kulperger, R. J. (1989) <https://www.jstor.org/stable/25050735> Rajarshi, M.B. (1990) <doi:10.1007/BF00050835> Dudek, A.E. Maiz, S. and Elbadaoui, M. (2014) <doi:10.1016/j.sigpro.2014.04.022> Beran R. (1986) <doi:10.1214/aos/1176349847> Maritz, J. S. and Jarrett, R. G. (1978) <doi:10.2307/2286545> Bertail, P., Politis, D., Romano, J. (1999) <doi:10.2307/2670177> Bertail, P. and Clémençon, S. (2006b) <doi:10.1007/0-387-36062-X_1> RaduloviÄ , D. (2004) <doi:10.1007/BF02603005> Hurd, H.L., Miamee, A.G. (2007) <doi:10.1002/9780470182833> Bühlmann, P. (1997) <doi:10.2307/3318584> Choi, E., Hall, P. (2000) <doi:10.1111/1467-9868.00244> Efron, B., Tibshirani, R. (1993, ISBN:9780429246593) Bertail, P., Clémençon, S. and Tressou, J. (2009) <doi:10.1007/s10687-009-0081-y> Bertail, P., Medina-Garay, A., De Lima-Medina, F. and Jales, I. (2024) <doi:10.1080/02331888.2024.2344670>.

Queries data from WHOIS servers.

This package selects genes associated with survival.

RubyRC4 is a pure Ruby implementation of the RC4 algorithm.

Fits linear models to repeated ordinal scores using GEE methodology.

Interface to the yacas computer algebra system (<http://www.yacas.org/>).

The goal of ralger is to facilitate web scraping in R.

rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data.

RJB is a bridge program that connects Ruby and Java via the Java Native Interface.

This package provides a web interface to compute transcriptional regulatory modules with rTRM.

R access to the FOAAS (F... Off As A Service) web service is provided.

Analyzes and predicts from matrix population models (Caswell 2006) <doi:10.1002/9781118445112.stat07481>.

RDF.rb is a pure-Ruby library for working with Resource Description Framework (RDF) data.

This package is used for the analysis of long-range chromatin interactions from 3C-seq assay.

This package provides an iteration of the DEoptim function. It performs global optimization by differential evolution.

Robust pairwise correlations based on estimates of scale, particularly on "FastQn" one-step M-estimate.

This package provides a friendly, object oriented API for creating PowerPoint slide decks in R.

Parser generator for R using combinatory parsers. It is inspired by combinatory parsers developed in Haskell.