Allows calculation on, and sampling from Gibbs Random Fields, and more precisely general homogeneous Potts model. The primary tool is the exact computation of the intractable normalising constant for small rectangular lattices. Beside the latter function, it contains method that give exact sample from the likelihood for small enough rectangular lattices or approximate sample from the likelihood using MCMC samplers for large lattices.

This package creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics.

Simulate expected equilibrium length composition, yield-per-recruit, and the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR model to length data to estimate selectivity, relative apical fishing mortality, and the spawning potential ratio for data-limited fisheries. See Hordyk et al (2016) <doi:10.1139/cjfas-2015-0422> for more information about the LBSPR assessment method.

Next-Generation Clustered Heat Maps (NG-CHMs) allow for dynamic exploration of heat map data in a web browser. NGCHM allows users to create both stand-alone HTML files containing a Next-Generation Clustered Heat Map, and .ngchm files to view in the NG-CHM viewer. See Ryan MC, Stucky M, et al (2020) <doi:10.12688/f1000research.20590.2> for more details.

K-nearest neighbor search for projected and non-projected sf spatial layers. Nearest neighbor search uses (1) C code from GeographicLib for lon-lat point layers, (2) function knn() from package nabor for projected point layers, or (3) function st_distance() from package sf for line or polygon layers. The package also includes several other utility functions for spatial analysis.

The aim of neo2R is to provide simple and low level connectors for querying neo4j graph databases (<https://neo4j.com/>). The objects returned by the query functions are either lists or data.frames with very few post-processing. It allows fast processing of queries returning many records. And it let the user handle post-processing according to the data model and his needs.

Provide methods for estimating optimal treatment regimes in survival contexts with Kaplan-Meier-like estimators when no unmeasured confounding assumption is satisfied (Jiang, R., Lu, W., Song, R., and Davidian, M. (2017) <doi:10.1111/rssb.12201>) and when no unmeasured confounding assumption fails to hold and a binary instrument is available (Xia, J., Zhan, Z., Zhang, J. (2022) <arXiv:2210.05538>).

Village potential statistics (PODES) collects various information on village potential and challenges faced by villages in Indonesia. Information related to village potential includes economy, security, health, employment, communication and information, sports, entertainment, development, community empowerment, education, socio-culture, transportation in the village. Information related to challenges includes natural disasters, public health, environmental pollution, social problems and security disturbances that occur in the village.

This package provides tools for writing and debugging R code. Provides: %.>% dot-pipe (an S3 configurable pipe), unpack/to (R style multiple assignment/return), build_frame()'/'draw_frame() ('data.frame example tools), qc() (quoting concatenate), := (named map builder), let() (converts non-standard evaluation interfaces to parametric standard evaluation interfaces, inspired by gtools::strmacro() and base::bquote()'), and more.

An R package that tests for enrichment and depletion of user-defined pathways using a Fisher's exact test. The method is designed for versatile pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run pathway analysis on custom annotations. This package is also integrated with Cytoscape to provide network-based pathway visualization that enhances the interpretability of the results.

This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another vignette compares outputs of Chapter 12 of the OSCA book with analogous outputs based on totalVI quantifications. Future work will address other components of scvi-tools, with a focus on building understanding of probabilistic methods based on variational autoencoders.

SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.

The TMSig package contains tools to prepare, analyze, and visualize named lists of sets, with an emphasis on molecular signatures (such as gene or kinase sets). It includes fast, memory efficient functions to construct sparse incidence and similarity matrices and filter, cluster, invert, and decompose sets. Additionally, bubble heatmaps can be created to visualize the results of any differential or molecular signatures analysis.

This package can do differential expression analysis of RNA-seq expression profiles with biological replication. It implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. It be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE.

This package is a ggplot2 extension. It provides some utility functions that do not entirely fit within the grammar of graphics concept. The package extends ggpplots facets through customisation, by setting individual scales per panel, resizing panels and providing nested facets. It also allows multiple colour, fill scales per plot and hosts a smaller collection of stats, geoms and axis guides.

This package provides data sets and scripts to accompany Time Series Analysis and Its Applications: With R Examples (4th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2017, https://doi.org/10.1007/978-3-319-52452-8, and Time Series: A Data Analysis Approach Using R. Chapman-Hall, 2019, https://doi.org/10.1201/9780429273285.

The Round Robin Database Tool (RRDtool) is a system to store and display time-series data (e.g. network bandwidth, machine-room temperature, server load average). It stores the data in Round Robin Databases (RRDs), a very compact way that will not expand over time. RRDtool processes the extracted data to enforce a certain data density, allowing for useful graphical representation of data values.

Download, prepare and analyze data from large-scale assessments and surveys with complex sampling and assessment design (see Rutkowski', 2010 <doi:10.3102/0013189X10363170>). Such studies are, for example, international assessments like TIMSS', PIRLS and PISA'. A graphical interface is available for the non-technical user.The package includes functions to covert the original data from SPSS into R data sets keeping the user-defined missing values, merge data from different respondents and/or countries, generate variable dictionaries, modify data, produce descriptive statistics (percentages, means, percentiles, benchmarks) and multivariate statistics (correlations, linear regression, binary logistic regression). The number of supported studies and analysis types will increase in future. For a general presentation of the package, see Mirazchiyski', 2021a (<doi:10.1186/s40536-021-00114-4>). For detailed technical aspects of the package, see Mirazchiyski', 2021b (<doi:10.3390/psych3020018>).

This package provides methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndtâ Hallâ Hallâ Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) <doi:10.1007/s00184-007-0130-y>. For an overview, we refer the reader to Fülle et al. (2024) <doi:10.18637/jss.v111.i04>.

Distributes Gaussian process calculations across nodes in a distributed memory setting, using Rmpi. The bigGP class provides high-level methods for maximum likelihood with normal data, prediction, calculation of uncertainty (i.e., posterior covariance calculations), and simulation of realizations. In addition, bigGP provides an API for basic matrix calculations with distributed covariance matrices, including Cholesky decomposition, back/forwardsolve, crossproduct, and matrix multiplication.

According to the code or the name of the administrative division at the county level and above provided by the Ministry of Civil Affairs of the People's Republic of China in 2022 (<https://www.mca.gov.cn/mzsj/xzqh/2022/202201xzqh.html>), get the map file online from the website of AutoNavi Map (<http://datav.aliyun.com/portal/school/atlas/area_selector>).

Estimation of the total population size from capture-recapture data efficiently and with low bias implementing the methods from Das M, Kennedy EH, and Jewell NP (2021) <arXiv:2104.14091>. The estimator is doubly robust against errors in the estimation of the intermediate nuisance parameters. Users can choose from the flexible estimation models provided in the package, or use any other preferred model.

This package provides functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), <DOI:10.1016/j.patrec.2017.08.019>. Performance criteria are added to compare similarity between two signals (query and reference).

Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. <doi:10.22271/09746315.2020.v16.i3.1358>.