Showing 8 of total 8 results (show query)
pln-team
PLNmodels:Poisson Lognormal Models
The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.
Maintained by Julien Chiquet. Last updated 5 days ago.
count-datamultivariate-analysisnetwork-inferencepcapoisson-lognormal-modelopenblascpp
55 stars 9.54 score 226 scripts
bioc
OUTRIDER:OUTRIDER - OUTlier in RNA-Seq fInDER
Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results.
Maintained by Christian Mertes. Last updated 5 months ago.
immunooncologyrnaseqtranscriptomicsalignmentsequencinggeneexpressiongeneticscount-datadiagnosticsexpression-analysismendelian-geneticsoutlier-detectionrna-seqopenblascpp
50 stars 9.07 score 110 scripts 1 dependents
rpact-com
rpact:Confirmatory Adaptive Clinical Trial Design and Analysis
Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) <doi:10.1007/978-3-319-32562-0>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.
Maintained by Friedrich Pahlke. Last updated 6 days ago.
adaptive-designanalysisclinical-trialscount-datagroup-sequential-designspower-calculationsample-size-calculationsimulationvalidatedfortrancpp
25 stars 8.20 score 110 scripts 1 dependents
geobosh
Countr:Flexible Univariate Count Models Based on Renewal Processes
Flexible univariate count models based on renewal processes. The models may include covariates and can be specified with familiar formula syntax as in glm() and package 'flexsurv'. The methodology is described by Kharrat et all (2019) <doi:10.18637/jss.v090.i13> (included as vignette 'Countr_guide' in the package). If the suggested package 'pscl' is not available from CRAN, it can be installed with 'remotes::install_github("cran/pscl")'. It is no longer used by the functions in this package but is needed for some of the extended examples.
Maintained by Georgi N. Boshnakov. Last updated 1 years ago.
count-datarenewal-processsports-modellingopenblascpp
4 stars 5.71 score 43 scripts
vpnsctl
mixpoissonreg:Mixed Poisson Regression for Overdispersed Count Data
Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.
Maintained by Alexandre B. Simas. Last updated 4 years ago.
count-datadiagnosticsinfluence-analysislocal-influencenegative-binomial-regressionpoisson-inverse-gaussian-regression
3 stars 5.44 score 23 scripts
biogenies
countfitteR:Comprehensive Automatized Evaluation of Distribution Models for Count Data
A large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set.
Maintained by Jaroslaw Chilimoniuk. Last updated 2 years ago.
cancercancer-imaging-researchcount-datacount-distributionfoci
4 stars 5.33 score 27 scripts
mfaymon
spINAR:(Semi)Parametric Estimation and Bootstrapping of INAR Models
Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>) for the semiparametric setting introduced in Drost et al. (2009) <doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR data (Jentsch, C. and Weiß, C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible simulation of INAR data.
Maintained by Maxime Faymonville. Last updated 11 months ago.
bootstrappingcount-dataparametric-estimationpenalizationsemiparametric-estimationsimulationtime-seriesvalidation
4 stars 5.20 score 7 scriptsbioc
ROSeq:Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data
ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used.
Maintained by Krishan Gupta. Last updated 5 months ago.
geneexpressiondifferentialexpressionsinglecellcount-datagene-expressiongene-expression-profilesnormalizationpopulationsranktmmtungtung-datasettutorialvignette
2 stars 4.34 score 11 scripts