This package is designed for typesetting the programmable elements in digital hardware, i.e., registers. Such registers typically have many fields and can be quite wide; they are thus a challenge to typeset in a consistent manner. Register is similar in some aspects to the bytefield and bitpattern packages. Anyone doing hardware documentation using LaTeX should examine those packages. An example Perl module and script are provided, to convert the register specifications into structures suitable for, say, a pre-silicon test environment.

The package curatedPCaData offers a selection of annotated prostate cancer datasets featuring multiple omics, manually curated metadata, and derived downstream variables. The studies are offered as MultiAssayExperiment (MAE) objects via ExperimentHub, and comprise of clinical characteristics tied to gene expression, copy number alteration and somatic mutation data. Further, downstream features computed from these multi-omics data are offered. Multiple vignettes help grasp characteristics of the various studies and provide example exploratory and meta-analysis of leveraging the multiple studies provided here-in.

TrainFastImputation() uses training data to describe a multivariate normal distribution that the data approximates or can be transformed into approximating and stores this information as an object of class FastImputationPatterns'. FastImputation() function uses this FastImputationPatterns object to impute (make a good guess at) missing data in a single line or a whole data frame of data. This approximates the process used by Amelia <https://gking.harvard.edu/amelia> but is much faster when filling in values for a single line of data.

Estimates unit-level and population-level parameters from a hierarchical model in marketing applications. The package includes: Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates. For more details, see Bumbaca, F. (Rico), Misra, S., & Rossi, P. E. (2020) <doi:10.1177/0022243720952410> "Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models". Journal of Marketing Research, 57(6), 999-1018.

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 It has been superseded by GeneralizedHyperbolic'.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.

This library is a collection of pseudo random number generators.

While Common Lisp does provide a RANDOM function, it does not allow the user to pass an explicit SEED, nor to portably exchange the random state between implementations. This can be a headache in cases like games, where a controlled seeding process can be very useful.

For both curiosity and convenience, this library offers multiple algorithms to generate random numbers, as well as a bunch of generally useful methods to produce desired ranges.

Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details.

The package provides access to the copy of the Synaptic proteome database. It was designed as an accompaniment for Synaptome.DB package. Database provides information for specific synaptic genes and allows building the protein-protein interaction graph for gene sets, synaptic compartments, and brain regions. In the current update we added 6 more synaptic proteome studies, which resulted in total of 64 studies. We introduced Synaptic Vesicle as a separate compartment. We also added coding mutations for Autistic Spectral disorder and Epilepsy collected from publicly available databases.

Creation of interactive tables, listings and figures ('TLFs') and associated report for exploratory analysis of data in a clinical trial, e.g. for clinical oversight activities. Interactive figures include sunburst, treemap, scatterplot, line plot and barplot of counts data. Interactive tables include table of summary statistics (as counts of adverse events, enrollment table) and listings. Possibility to compare data (summary table or listing) across two data batches/sets. A clinical data review report is created via study-specific configuration files and template R Markdown reports contained in the package.

Design parameters of the optimal two-period multiarm platform design (controlling for either family-wise error rate or pair-wise error rate) can be calculated using this package, allowing pre-planned deferred arms to be added during the trial. More details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J. (2022) "An optimal two-period multiarm platform design with new experimental arms added during the trial". Manuscript submitted for publication. For additional references: Dunnett, C. W. (1955) <doi:10.2307/2281208>.

The AFfunction() is a function which returns an estimate of the Attributable Fraction (AF) and a plot of the AF as a function of heritability, disease prevalence, size of target group and intervention effect. Since the AF is a function of several factors, a shiny app is used to better illustrate how the relationship between the AF and heritability depends on several other factors. The app is ran by the function runShinyApp(). For more information see Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.

Allows the user to perform approximate matching between the occupational classifications using concordances provided by the Institute for Structural Research and Faculty of Economics, University of Warsaw, <doi:10.1111/ecot.12145>. The crosswalks offer a complete step-by-step mapping of Standard Occupational Classification (2010) data to the International Standard Classification of Occupations (2008). We propose a mapping method based on the aforementioned research that converts measurements to the smallest possible unit of the target taxonomy, and then performs an aggregation/estimate to the requested degree Occupational Hierarchical level.

LaBB-CAT is a web-based language corpus management system developed by the New Zealand Institute of Language, Brain and Behaviour (NZILBB) - see <https://labbcat.canterbury.ac.nz>. This package defines functions for accessing corpus data in a LaBB-CAT instance. You must have at least version 20230224.1731 of LaBB-CAT to use this package. For more information about LaBB-CAT', see Robert Fromont and Jennifer Hay (2008) <doi:10.3366/E1749503208000142> or Robert Fromont (2017) <doi:10.1016/j.csl.2017.01.004>.

Estimate and understand individual-level variation in transmission. Implements density and cumulative compound Poisson discrete distribution functions ('Kremer et al. (2021) <doi:10.1038/s41598-021-93578-x>), as well as functions to calculate infectious disease outbreak statistics given epidemiological parameters on individual-level transmission; including the probability of an outbreak becoming an epidemic/extinct ('Kucharski et al. (2020) <doi:10.1016/S1473-3099(20)30144-4>), or the cluster size statistics, e.g. what proportion of cases cause X\% of transmission ('Lloyd-Smith et al. (2005) <doi:10.1038/nature04153>).

TaskJuggler (tj3) is a project management tool for project planning and tracking using a domain specific language; projects are plain text files written using your favourite text editor. It includes reporting in HTML, CSV or iCalendar format and an email based status tracking system to send and receive time sheets from collaborators.

It covers the complete spectrum of project management tasks from the first idea to the completion of the project. It assists you during project scoping, resource assignment, cost and revenue planning, risk and communication management, status tracking and reporting.

It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of maximum entropy for the purpose of making a classification between the classes of the sequences. There are two data present in the BASiNET package, "mRNA", and "ncRNA" with 10 sequences. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples.

This package provides tools for sensitivity analysis of LSD simulation models. Reads object-oriented data produced by LSD simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) <doi:10.1016/j.ejor.2007.10.013>) is estimated using the simulation data to provide the data required by the Sobol decomposition. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).

This package provides functions to aid in the design and analysis of agronomic and agricultural experiments through easy access to documentation and helper functions, especially for users who are learning these concepts. While not required for most functionality, this package enhances the `asreml` package which provides a computationally efficient algorithm for fitting mixed models using Residual Maximum Likelihood. It is a commercial package that can be purchased as asreml-R from VSNi <https://vsni.co.uk/>, who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>).

This package provides functions for converting time series of spatial abundance or density data in raster format to vector fields of population movement using the digital image correlation technique. More specifically, the functions in the package compute cross-covariance using discrete fast Fourier transforms for computational efficiency. Vectors in vector fields point in the direction of highest two dimensional cross-covariance. The package has a novel implementation of the digital image correlation algorithm that is designed to detect persistent directional movement when image time series extend beyond a sequence of two raster images.

The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with various scenarios. Richard J. Barker, William A. Link (2013) <doi:10.1080/00031305.2013.791644>. Thomas A. Murray, Ying Yuan, Peter F. Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) <doi:10.1111/biom.12842>. Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) <arXiv:2108.06568>.

Provide the safe color set for color blindness, the simulator of protanopia, deuteranopia. The color sets are collected from: Wong, B. (2011) <doi:10.1038/nmeth.1618>, and <http://mkweb.bcgsc.ca/biovis2012/>. The simulations of the appearance of the colors to color-deficient viewers were based on algorithms in Vienot, F., Brettel, H. and Mollon, J.D. (1999) <doi:10.1002/(SICI)1520-6378(199908)24:4%3C243::AID-COL5%3E3.0.CO;2-3>. The cvdPlot() function to generate ggplot grobs of simulations were modified from <https://github.com/clauswilke/colorblindr>.

Create a custom sized Core Collection based on a distance matrix and applying the A-NE (accession nearest entry), E-NE (entry nearest entry) or E-E (entry entry) method as introduced in Jansen and van Hintum (2007) <doi:10.1007/s00122-006-0433-9> and further elaborated on in Odong, T.L. (2012) <https://edepot.wur.nl/212422>. Optionally a list of preselected accessions to be included into the core can be set. For each accession in the computed core, if available nearby accessions are retrievable that can be used as an alternative.

This package performs analysis of popular experimental designs used in the field of biological research. The designs covered are completely randomized design, randomized complete block design, factorial completely randomized design, factorial randomized complete block design, split plot design, strip plot design and latin square design. The analysis include analysis of variance, coefficient of determination, normality test of residuals, standard error of mean, standard error of difference and multiple comparison test of means. The package has functions for transformation of data and yield data conversion. Some datasets are also added in order to facilitate examples.

Aids in the analysis of genes influencing cancer survival by including a principal function, calculator(), which calculates the P-value for each provided gene under the optimal cutoff in cancer survival studies. Grounded in methodologies from significant works, this package references Therneau's survival package (Therneau, 2024; <https://CRAN.R-project.org/package=survival>) and the survival analysis extensions by Therneau and Grambsch (2000, ISBN 0-387-98784-3). It also integrates the survminer package by Kassambara et al. (2021; <https://CRAN.R-project.org/package=survminer>), enhancing survival curve visualizations with ggplot2'.