This package provides a toolkit for the analysis and management of data for genes in the so-called "Human Leukocyte Antigen" (HLA) region. Functions extract reference data from the Anthony Nolan HLA Informatics Group/ImmunoGeneTics HLA GitHub repository (ANHIG/IMGTHLA) <https://github.com/ANHIG/IMGTHLA>, validate Genotype List (GL) Strings, convert between UNIFORMAT and GL String Code (GLSC) formats, translate HLA alleles and GLSCs across ImmunoPolymorphism Database (IPD) IMGT/HLA Database release versions, identify differences between pairs of alleles at a locus, generate customized, multi-position sequence alignments, trim and convert allele-names across nomenclature epochs, and extend existing data-analysis methods.

This package provides a protocol that facilitates the processing and analysis of Hydrogen-Deuterium Exchange Mass Spectrometry data using p-value statistics and Critical Interval analysis. It provides a pipeline for analyzing data from HDXExaminer (Sierra Analytics, Trajan Scientific), automating matching and comparison of protein states through Welch's T-test and the Critical Interval statistical framework. Additionally, it simplifies data export, generates PyMol scripts, and ensures calculations meet publication standards. HDXBoxeR assists in various aspects of hydrogen-deuterium exchange data analysis, including reprocessing data, calculating parameters, identifying significant peptides, generating plots, and facilitating comparison between protein states. For details check papers by Hageman and Weis (2019) <doi:10.1021/acs.analchem.9b01325> and Masson et al. (2019) <doi:10.1038/s41592-019-0459-y>. HDXBoxeR citation: Janowska et al. (2024) <doi:10.1093/bioinformatics/btae479>.

Spatial heterogeneity can be specified in various ways. hspm is an ambitious project that aims at implementing various methodologies to control for heterogeneity in spatial models. The current version of hspm deals with spatial and (non-spatial) regimes models. In particular, the package allows to estimate a general spatial regimes model with additional endogenous variables, specified in terms of a spatial lag of the dependent variable, the spatially lagged regressors, and, potentially, a spatially autocorrelated error term. Spatial regime models are estimated by instrumental variables and generalized methods of moments (see Arraiz et al., (2010) <doi:10.1111/j.1467-9787.2009.00618.x>, Bivand and Piras, (2015) <doi:10.18637/jss.v063.i18>, Drukker et al., (2013) <doi:10.1080/07474938.2013.741020>, Kelejian and Prucha, (2010) <doi:10.1016/j.jeconom.2009.10.025>).

An R API wrapper for the Hystreet project <https://hystreet.com>. Hystreet provides pedestrian counts in different cities in Germany.

Structural handling of Finnish identity codes (natural persons and organizations); extract information, check ID validity and diagnostics.

Analysis of Surface Plasmon Resonance (SPR) and Biolayer Interferometry data, with automations for high-throughput SPR. This version of the package fits the 1: 1 binding model, with and without bulkshift. It offers optional local or global Rmax fitting. The user must provide a sample sheet and a Carterra output file in Carterra's current format. There is a utility function to convert from Carterra's old output format. The user may run a custom pipeline or use the provided Runscript', which will produce a pdf file containing fitted Rmax, ka, kd and standard errors, a plot of the sensorgram and fits, and a plot of residuals. The script will also produce a .csv file with all of the relevant parameters for each spot on the SPR chip.

Computes heteroskedasticity-consistent covariance matrix estimators for ordinary least squares regression models. The published HC0 through HC5m estimators implemented in the package follow White (1980) <doi:10.2307/1912934>, Hinkley (1977) <doi:10.1080/00401706.1977.10489550>, Horn et al. (1975) <doi:10.1080/01621459.1975.10479877>, MacKinnon and White (1985) <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004) <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto and da Silva (2011) <doi:10.1007/s10182-010-0141-2>, Cribari-Neto et al. (2007) <doi:10.1080/03610920601126589>, and Li et al. (2016) <doi:10.1080/00949655.2016.1198906>. The package also includes HCbeta, a new estimator proposed by the package authors. It provides normal Wald tests, confidence intervals, diagnostics, and S3 output for applied inference.

Harmony is a tool using AI which allows you to compare items from questionnaires and identify similar content. You can try Harmony at <https://harmonydata.ac.uk/app/> and you can read our blog at <https://harmonydata.ac.uk/blog/> or at <https://fastdatascience.com/how-does-harmony-work/>. Documentation at <https://harmonydata.ac.uk/harmony-r-released/>.

This package provides tools for emitting the Problem Details structure defined in RFC 7807 <https://tools.ietf.org/html/rfc7807> for reporting errors from HTTP servers in a standard way.

This package provides a Shiny app allowing to convert HTML code to R code (e.g. <span>Hello</span> to tags$span("Hello")'), for usage in a Shiny UI.

Makes it easy to download a large number of files such as PDF files and CSV files, while automatically slowing down requests, letting you know where it is up to, and adjusting for files that have already been downloaded.

This package provides a method for identifying responses to experimental stimulation in mass or flow cytometry that uses high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach. In the context of in vitro stimulation assays where high-parameter cytometry was used to monitor intracellular response markers, using cell populations annotated either through automated clustering or manual gating for a combined set of stimulated and unstimulated samples, HDStIM labels cells as responding or non-responding. The package also provides auxiliary functions to rank intracellular markers based on their contribution to identifying responses and generating diagnostic plots.

Palettes generated from Hello Kitty cartoon.

Perform statistical writership analysis of scanned handwritten documents with a shiny app for handwriter'.

This package provides access to datasets published by Hlà daÄ státu <https://www.hlidacstatu.cz/>, a Czech watchdog, via their API.

This package provides tools to estimate, compare, and visualize healthcare resource utilization using data derived from electronic health records or real-world evidence sources. The package supports pre index and post index analysis, patient cohort comparison, and customizable summaries and visualizations for clinical and health economics research. Methods implemented are based on Scott et al. (2022) <doi:10.1080/13696998.2022.2037917> and Xia et al. (2024) <doi:10.14309/ajg.0000000000002901>.

Format quantities of time or bytes into human-friendly strings.

This package implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.

Builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis. The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum.

This package contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) <doi:10.1111/rssc.12194>; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) <doi:10.1029/2017JB015360>. The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package HiddenMarkov by David Harte. This updated version has included an example dataset and R code examples to show how to transform the data into the objects needed in the main functions. We have also made changes to increase the speed of some of the functions.

Decode elements of the Australian Higher Education Information Management System (HEIMS) data for clarity and performance. HEIMS is the record system of the Department of Education, Australia to record enrolments and completions in Australia's higher education system, as well as a range of relevant information. For more information, including the source of the data dictionary, see <http://heimshelp.education.gov.au/sites/heimshelp/dictionary/pages/data-element-dictionary>.

Maintenance has been discontinued for this package. It has been superseded by GeneralizedHyperbolic'. GeneralizedHyperbolic includes all the functionality of HyperbolicDist and more and is based on a more rational design. HyperbolicDist provides functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, including fitting of the hyperbolic to data.

Pfafstetter Hydrological Codes as cited in Verdin and Verdin (1999) <doi: 10.1016/S0022-1694(99)00011-6> are decoded for upstream or downstream queries.

R interface for H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).