Algorithms of nonparametric sequential test and online change-point detection for streams of univariate (sub-)Gaussian, binary, and bounded random variables, introduced in following publications - Shin et al. (2024) <doi:10.48550/arXiv.2203.03532>, Shin et al. (2021) <doi:10.48550/arXiv.2010.08082>.

Estimate networks and causal relationships in complex systems through Structural Equation Modeling. This package also includes functions for importing, weight, manipulate, and fit biological network models within the Structural Equation Modeling framework as outlined in the Supplementary Material of Grassi M, Palluzzi F, Tarantino B (2022) <doi:10.1093/bioinformatics/btac567>.

Created for population health analytics and monitoring. The functions in this package work best when working with patient level Master Patient Index-like datasets . Built to be used by NHS bodies and other health service providers.

This package provides functions to visually and statistically analyze single system data.

This package provides a major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml().

This package provides functions to speed up work flow for hydrological analysis. Focused on Australian climate data (SILO climate data), hydrological models (eWater Source) and in particular South Australia (<https://water.data.sa.gov.au> hydrological data).

Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide.

Implementation of the SAM prior and generation of its operating characteristics for dynamically borrowing information from historical data. For details, please refer to Yang et al. (2023) <doi:10.1111/biom.13927>.

This package provides tools for creating and working with survey replicate weights, extending functionality of the survey package from Lumley (2004) <doi:10.18637/jss.v009.i08>. Implements bootstrap methods for complex surveys, including the generalized survey bootstrap as described by Beaumont and Patak (2012) <doi:10.1111/j.1751-5823.2011.00166.x>. Methods are provided for applying nonresponse adjustments to both full-sample and replicate weights as described by Rust and Rao (1996) <doi:10.1177/096228029600500305>. Implements methods for sample-based calibration described by Opsomer and Erciulescu (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021002/article/00006-eng.htm>. Diagnostic functions are included to compare weights and weighted estimates from different sets of replicate weights.

This package provides functions for statistical analysis of point processes.

This package provides a coherent interface for exploring and transforming multiple related data frames that share a common structure. Complements single-dataset inspection tools by operating across an entire collection at once. Also includes lightweight utilities for related file and folder management tasks.

Compute the frequency distribution of a search term in a series of texts. For example, Arthur Conan Doyle wrote a total of 60 Sherlock Holmes stories, comprised of 54 short stories and 4 longer novels. I wanted to test my own subjective impression that, in many of the stories, Sherlock Holmes popularity was used as bait to induce the reader to read a story that is essentially not primarily a Sherlock Holmes story. I used the term "Holmes" as a search pattern, since Watson would frequently address him by name, or use his name to describe something that he was doing. My hypothesis is that the frequency distribution of the search pattern "Holmes" is a good proxy for the degree to which a story is or is not truly a Sherlock Holmes story. The results are presented in a manuscript that is available as a vignette and online at <https://barryzee.github.io/Concordance/index.html>.

This package contains an R Markdown template for a clinical trial protocol adhering to the SPIRIT statement. The SPIRIT (Standard Protocol Items for Interventional Trials) statement outlines recommendations for a minimum set of elements to be addressed in a clinical trial protocol. Also contains functions to create a xml document from the template and upload it to clinicaltrials.gov<https://www.clinicaltrials.gov/> for trial registration.

Cluster-independent method based on topology structure of gene co-expression network for identifying feature gene sets, extracting cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Without prior cell clustering, SifiNet circumvents potential inaccuracies in clustering that may influence subsequent analyses. This method is introduced in Qi Gao, Zhicheng Ji, Liuyang Wang, Kouros Owzar, Qi-Jing Li, Cliburn Chan, Jichun Xie "SifiNet: a robust and accurate method to identify feature gene sets and annotate cells" (2024) <doi:10.1093/nar/gkae307>.

Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) <doi:10.1111/j.1755-0998.2011.03108.x>.

This package provides a workflow based on scTenifoldNet to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the geneâ s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the geneâ s function in the analyzed cells.

Lexicons and tools to perform sentiment analysis on Italian texts. Lexicons included: Sentix 3.0, MAL, ElIta VAD and basic emotions (Plutchik's wheel of emotions). For more details about the lexicons, see Basile & Nissim (2013), "Sentiment Analysis on Italian Tweets", <https://aclanthology.org/W13-1614/>; Vassallo et al. (2019), "The Tenuousness of Lemmatization in Lexicon-based Sentiment Analysis", <https://aclanthology.org/2019.clicit-1.79/>; Di Palma (2024), "ELIta: A New Italian Language Resource for Emotion Analysis", <https://aclanthology.org/2024.clicit-1.36/>.

This package provides a generalization of the statistic used in Friedman's ANOVA method and in Durbin's rank test. This nonparametric statistical test is useful for the data obtained from block designs with missing observations occurring randomly. A resulting p-value is based on the chi-squared distribution and Monte Carlo method.

Collect your data on digital marketing campaigns from Snapchat Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

Wrapper for the non-validating SQL parser Python module sqlparse <https://github.com/andialbrecht/sqlparse>. It allows parsing, splitting, and formatting SQL statements.

This package implements the SVM-Maj algorithm to train data with support vector machine <doi:10.1007/s11634-008-0020-9>. This algorithm uses two efficient updates, one for linear kernel and one for the nonlinear kernel.

An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.

The primary goal of Serpstat API <https://api-docs.serpstat.com/docs/serpstat-public-api/jenasqbwtxdlr-introduction-to-serpstat-api> is to reduce manual SEO (search engine optimization) and PPC (pay-per-click) tasks. You can automate your keywords research or competitors analysis with this API wrapper.

This package performs sensitivity analysis for Structural Equation Modeling (SEM). It determines which sample points need to be removed for the sign of a specific path in the SEM model to change, thus assessing the robustness of the model. Methodological manuscript in preparation.