Two papers published in the early 2000's (Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) and (Zeeberg, B.R., Qin, H., Narashimhan, S., et al. (2005) <doi:10.1186/1471-2105-6-168>) implement GoMiner and High Throughput GoMiner ('HTGM') to map lists of genes to the Gene Ontology (GO) <https://geneontology.org>. Until recently, these were hosted on a server at The National Cancer Institute (NCI). In order to continue providing these services to the bio-medical community, I have developed stand-alone versions. The current package HTGM builds upon my recent package GoMiner'. The output of GoMiner is a heatmap showing the relationship of a single list of genes and the significant categories into which they map. High Throughput GoMiner ('HTGM') integrates the results of the individual GoMiner analyses. The output of HTGM is a heatmap showing the relationship of the significant categories derived from each gene list. The heatmap has only 2 axes, so the identity of the genes are unfortunately "integrated out of the equation." Because the graphic for the heatmap is implemented in Scalable Vector Graphics (SVG) technology, it is relatively easy to hyperlink each picture element to the relevant list of genes. By clicking on the desired picture element, the user can recover the "lost" genes.

This package provides functions to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate using censored (survival data), and to test for heterogeneity across multiple time points. More details are available in Parast et al (2024) <doi:10.1002/sim.10122>.

This package provides a set of R functions which implements Hotelling's T^2 test and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations.

This package provides a modular and computationally efficient R package for parameterizing, simulating, and analyzing health economic simulation models. The package supports cohort discrete time state transition models (Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>, N-state partitioned survival models (Glasziou et al. 1990) <doi:10.1002/sim.4780091106>, and individual-level continuous time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>, encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, <doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table make individual-patient simulation, probabilistic sensitivity analysis, and incorporation of patient heterogeneity fast.

This package provides a local haplotyping tool for use in trait association and trait prediction analyses pipelines. HaploVar enables users take single nucleotide polymorphisms (SNPs) (in VCF format) and a linkage disequilibrium (LD) matrix, calculate local haplotypes and format the output to be compatible with a wide range of trait association and trait prediction tools. The local haplotypes are calculated from the LD matrix using a clustering algorithm called density-based spatial clustering of applications with noise ('DBSCAN') (Ester et al., 1996) <ISBN: 1577350049>.

This package contains most of the hex font files from the GNU Unifont Project <https://unifoundry.com/unifont/> compressed by xz'. GNU Unifont is a duospaced bitmap font that attempts to cover all the official Unicode glyphs plus several of the artificial scripts in the (Under-)ConScript Unicode Registry <https://www.kreativekorp.com/ucsur/>. Provides a convenience function for loading in several of them at the same time as a bittermelon bitmap font object for easy rendering of the glyphs in an R terminal or graphics device.

Read, plot, manipulate and process hydro-meteorological data records (with special features for Argentina and Chile data-sets).

Enhance package testthat by allowing tests to be attached to the function/object they test. This allows to keep functional and unit test code together.

Compute duration curves of daily flow series, both real and modeled, to be compared through indexes of flow duration curves. The package functions include comparative plots and goodness of fit tests. Flow duration curve indexes are based on: Yilmaz et al., (2008) <DOI:10.1029/2007WR006716>.

Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) <https://jmlr.org/papers/v18/16-342.html>.

Reliability Analysis and Maintenance Optimization using Hidden Markov Models (HMM). The use of HMMs to model the state of a system which is not directly observable and instead certain indicators (signals) of the true situation are provided via a control system. A hidden model can provide key information about the system dependability, such as the reliability of the system and related measures. An estimation procedure is implemented based on the Baum-Welch algorithm. Classical structures such as K-out-of-N systems and Shock models are illustrated. Finally, the maintenance of the system is considered in the HMM context and two functions for new preventive maintenance strategies are considered. Maintenance efficiency is measured in terms of expected cost. Methods are described in Gamiz, Limnios, and Segovia-Garcia (2023) <doi:10.1016/j.ejor.2022.05.006>.

Conducts analyses for healthcare program evaluations or intervention studies. Calculates regression analyses for standard ordinary least squares (OLS or linear) or logistic models. Performs regression models used for causal modeling such as differences-in-differences (DID) and interrupted time series (ITS) models. Provides limited interpretations of model results and a ranking of variable importance in models. Performs propensity score models, top-coding of model outcome variables, and can return new data with the newly formed variables. Also performs Cronbach's alpha for various scale items (e.g., survey questions). See Github URL for examples in the README file. For more details on the statistical methods, see Allen & Yen (1979, ISBN:0-8185-0283-5), Angrist & Pischke (2009, ISBN:9780691120355), Harrell (2016, ISBN:978-3-319-19424-0), Kline (1999, ISBN:9780415211581), Linden (2015) <doi:10.1177/1536867X1501500208>, Merlo (2006) <doi:10.1136/jech.2004.029454> Muthen & Satorra (1995) <doi:10.2307/271070>, and Rabe-Hesketh & Skrondal (2008, ISBN:978-1-59718-040-5).

This package provides a utility to quickly obtain clean and tidy men's basketball play by play data. Provides functions to access live play by play and box score data from ESPN<https://www.espn.com> with shot locations when available. It is also a full NBA Stats API<https://www.nba.com/stats/> wrapper. It is also a scraping and aggregating interface for Ken Pomeroy's men's college basketball statistics website<https://kenpom.com>. It provides users with an active subscription the capability to scrape the website tables and analyze the data for themselves.

In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. Reference: Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, <doi:10.1111/biom.13426>.

This package provides a Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.

This package provides various tests for comparing high-dimensional mean vectors in two sample populations.

Empirical value of the Hellinger correlation, a measure of dependence between two continuous random variables. More details can be found in Geenens and Lafaye De Micheaux (2019) <arXiv:1810.10276v4>.

Using hybrid data, this package created a vividly colored hybrid heat map. The input is two files which are auto-selected. The first file has three columns, the first two for pairs of species, with the third column for the hybrid experiment code (an integer). The second file is a list of code and their descriptions in two columns. The output is a figure showing the hybrid heat map with a color legend.

Probabilistic models describing the behavior of workload and queue on a High Performance Cluster and computing GRID under FIFO service discipline basing on modified Kiefer-Wolfowitz recursion. Also sample data for inter-arrival times, service times, number of cores per task and waiting times of HPC of Karelian Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011. Functions provided to import/export workload traces in Standard Workload Format (swf). Stability condition of the model may be verified either exactly, or approximately. Stability analysis: see Rumyantsev and Morozov (2017) <doi:10.1007/s10479-015-1917-2>, workload recursion: see Rumyantsev (2014) <doi:10.1109/PDCAT.2014.36>.

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.

Plot an R package's recursive dependency graph and tabulate the number of unique downstream dependencies added by top-level dependencies. This helps R package developers identify which of their declared dependencies add the most downstream dependencies in order to prioritize them for removal if needed. Uses graph stress minimization adapted from Schoch (2023) <doi:10.21105/joss.05238> and originally reported in Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>.

Compute house price indexes and series using a variety of different methods and models common through the real estate literature. Evaluate index goodness based on accuracy, volatility and revision statistics. Background on basic model construction for repeat sales models can be found at: Case and Quigley (1991) <https://ideas.repec.org/a/tpr/restat/v73y1991i1p50-58.html> and for hedonic pricing models at: Bourassa et al (2006) <doi:10.1016/j.jhe.2006.03.001>. The package author's working paper on the random forest approach to house price indexes can be found at: <http://www.github.com/andykrause/hpi_research>.

Estimate parameters of the hysteretic threshold autoregressive (HysTAR) model, using conditional least squares. In addition, you can generate time series data from the HysTAR model. For details, see Li, Guan, Li and Yu (2015) <doi:10.1093/biomet/asv017>.

This package provides a suite of diagnostic tools for hierarchical (multilevel) linear models. The tools include not only leverage and traditional deletion diagnostics (Cook's distance, covratio, covtrace, and MDFFITS) but also convenience functions and graphics for residual analysis. Models can be fit using either lmer in the lme4 package or lme in the nlme package.