Reproducible and automated analysis of multiplex bead assays such as CBA (Morgan et al. 2004; <doi: 10.1016/j.clim.2003.11.017>), LEGENDplex (Yu et al. 2015; <doi: 10.1084/jem.20142318>), and MACSPlex (Miltenyi Biotec 2014; Application note: Data acquisition and analysis without the MACSQuant analyzer; <https://www.miltenyibiotec.com/upload/assets/IM0021608.PDF>). The package provides functions for streamlined reading of fcs files, and identification of bead clusters and analyte expression. The package eases the calculation of standard curves and the subsequent calculation of the analyte concentration.

Quickly estimate the net growth rate of a population or clone whose growth can be approximated by a birth-death branching process. Input should be phylogenetic tree(s) of clone(s) with edge lengths corresponding to either time or mutations. Based on coalescent results in Johnson et al. (2023) <doi:10.1093/bioinformatics/btad561>. Simulation techniques as well as growth rate methods build on prior work from Lambert A. (2018) <doi:10.1016/j.tpb.2018.04.005> and Stadler T. (2009) <doi:10.1016/j.jtbi.2009.07.018>.

Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items (Battauz (2013) <doi:10.1007/s11336-012-9316-y>, Battauz (2015) <doi:10.18637/jss.v068.i07>). Test scoring can be performed by true score equating and observed score equating methods. DIF detection can be performed using a Wald-type test (Battauz (2019) <doi:10.1007/s10260-018-00442-w>). The package includes tests to assess the stability of the equating transformations (Battauz(2022) <doi:10.1111/stan.12277>).

Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. All estimates are obtained uniquely for each individual in the final models. The method for the graphicalVAR approach is found in Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

Set of tools for descriptive analysis of metaproteomics data generated from high-throughput mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, expressed as spectral counts, and to manipulate them in groups of metaproteins. This information can be represented using multiple visualization functions to portray the global metaproteome landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, taxonomic levels and/or functional annotation. The provided tools allow to implement flexible analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.

Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.

Reads/write binary genotype file compatible with PLINK <https://www.cog-genomics.org/plink/1.9/input#bed> into/from a R matrix; traverse genotype data one windows of variants at a time, like apply() or a for loop; reads/writes genotype relatedness/kinship matrices created by PLINK <https://www.cog-genomics.org/plink/1.9/distance#make_rel> or GCTA <https://cnsgenomics.com/software/gcta/#MakingaGRM> into/from a R square matrix. It is best used for bringing data produced by PLINK and GCTA into R workflow.

This package provides a variety of tools relevant to the analysis of marine soundscape data. There are tools for downloading AIS (automatic identification system) data from Marine Cadastre <https://hub.marinecadastre.gov>, connecting AIS data to GPS coordinates, plotting summaries of various soundscape measurements, and downloading relevant environmental variables (wind, swell height) from the National Center for Atmospheric Research data server <https://rda.ucar.edu/datasets/ds084.1/>. Most tools were developed to work well with output from Triton software, but can be adapted to work with any similar measurements.

This package provides functions for the evaluation of surrogate endpoints when both the surrogate and the true endpoint are failure time variables. The approaches implemented are: (1) the two-step approach (Burzykowski et al, 2001) <DOI:10.1111/1467-9876.00244> with a copula model (Clayton, Plackett, Hougaard) at the first step and either a linear regression of log-hazard ratios at the second step (either adjusted or not for measurement error); (2) mixed proportional hazard models estimated via mixed Poisson GLM (Rotolo et al, 2017 <DOI:10.1177/0962280217718582>).

Quickly and flexibly calculates weights for survey data, in order to correct for survey non-response or other sampling issues. Uses rake weighting, a common technique also know as rim weighting or iterative proportional fitting. This technique allows for weighting on multiple variables, even when the interlocked distribution of the two variables is not known. Interacts with Thomas Lumley's survey package, as described in Lumley, Thomas (2011, ISBN:978-1-118-21093-2). Adds additional functionality, more adaptable syntax, and error-checking to the base weighting functionality in survey.'.

Statistics students often have problems understanding the relation between a random variable's true scale and its z-values. To allow instructors to better better visualize histograms for these students, the package provides histograms with two horizontal axis containing z-values and the true scale of the variable. The function TeachHistDens() provides a density histogram with two axis. TeachHistCounts() and TeachHistRelFreq() are variations for count and relative frequency histograms, respectively. TeachConfInterv() and TeachHypTest() help instructors to visualize confidence levels and the results of hypothesis tests.

This package provides a collection of functions to make R a more effective viewscape analysis tool for calculating viewscape metrics based on computing the viewable area for given a point/multiple viewpoints and a digital elevation model.The method of calculating viewscape metrics implemented in this package are based on the work of Tabrizian et al. (2020) <doi:10.1016/j.landurbplan.2019.103704>. The algorithm of computing viewshed is based on the work of Franklin & Ray. (1994) <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=555780f6f5d7e537eb1edb28862c86d1519af2be>.

This package is intended to fill the role of conventional cytometry pre-processing software, for spectral decomposition, transformation, visualization and cleanup, and to aid further downstream analyses, such as with DepecheR, by enabling transformation of flowFrames and flowSets to dataframes. Functions for flowCore-compliant automatic 1D-gating/filtering are in the pipe line. The package name has been chosen both as it will deal with spectral cytometry and as it will hopefully give the user a nice pair of spectacles through which to view their data.

scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions.

This package provides methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance is used.

This package provides pure C++ implementations for reading and writing several common data formats based on Google protocol-buffers. It currently supports rexp.proto for serialized R objects, geobuf.proto for binary geojson, and mvt.proto for vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, hence the entire serialization is optimized at compile time. The RProtoBuf package on the other hand uses the protobuf runtime library to provide a general-purpose toolkit for reading and writing arbitrary protocol-buffer data in R.

This package implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data.

Data quality assessments guided by a data quality framework introduced by Schmidt and colleagues, 2021 <doi:10.1186/s12874-021-01252-7> target the data quality dimensions integrity, completeness, consistency, and accuracy. The scope of applicable functions rests on the availability of extensive metadata which can be provided in spreadsheet tables. Either standardized (e.g. as html5 reports) or individually tailored reports can be generated. For an introduction into the specification of corresponding metadata, please refer to the package website <https://dataquality.qihs.uni-greifswald.de/VIN_Annotation_of_Metadata.html>.

Alpha and beta diversity for taxonomic (TD), functional (FD), and phylogenetic (PD) dimensions based on rasters. Spatial and temporal beta diversity can be partitioned into replacement and richness difference components. It also calculates standardized effect size for FD and PD alpha diversity and the average individual traits across multilayer rasters. The layers of the raster represent species, while the cells represent communities. Methods details can be found at Cardoso et al. 2022 <https://CRAN.R-project.org/package=BAT> and Heming et al. 2023 <https://CRAN.R-project.org/package=SESraster>.

This package implements the methods of McGrath et al. (2020) <doi:10.1177/0962280219889080> and Cai et al. (2021) <doi:10.1177/09622802211047348> for estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis. These methods can be applied to studies that report the sample median, sample size, and one or both of (i) the sample minimum and maximum values and (ii) the first and third quartiles. The corresponding standard error estimators described by McGrath et al. (2023) <doi:10.1177/09622802221139233> are also included.

Kiener distributions K1, K2, K3, K4 and K7 to characterize distributions with left and right, symmetric or asymmetric fat tails in finance, neuroscience and other disciplines. Two algorithms to estimate the distribution parameters, quantiles, value-at-risk and expected shortfall. IMPORTANT: Standardization has been changed in versions >= 2.0.0 to get sd = 1 when kappa = Inf rather than 2*pi/sqrt(3) in versions <= 1.8.6. This affects parameter g (other parameters stay unchanged). Do not update if you need consistent comparisons with previous results for the g parameter.

Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). This package provides implementations of PLS-SVD, guided-PLS, and guided-PCA for supervised dimensionality reduction. The guided-PCA function (new in v1.1.0) automatically handles mixed data types (continuous and categorical) in the supervision matrix and provides detailed contribution analysis for interpretability. For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.

Make R scripts reproducible, by ensuring that every time a given script is run, the same version of the used packages are loaded (instead of whichever version the user running the script happens to have installed). This is achieved by using the command groundhog.library() instead of the base command library(), and including a date in the call. The date is used to call on the same version of the package every time (the most recent version available at that date). Load packages from CRAN, GitHub, or Gitlab.

This package provides a collection of functions for working with time series data, including functions for drawing, decomposing, and forecasting. Includes capabilities to compare multiple series and fit both additive and multiplicative models. Used by iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Holt (1957) <doi:10.1016/j.ijforecast.2003.09.015>, Winters (1960) <doi:10.1287/mnsc.6.3.324>, Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess".