Showing 16 of total 16 results (show query)
bioc
ALDEx2:Analysis Of Differential Abundance Taking Sample and Scale Variation Into Account
A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test (via aldex.corr). All tests report predicted p-values and posterior Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs. ALDEx2 can now be used to estimate the effect of scale on the results and report on the scale-dependent robustness of results.
Maintained by Greg Gloor. Last updated 5 months ago.
differentialexpressionrnaseqtranscriptomicsgeneexpressiondnaseqchipseqbayesiansequencingsoftwaremicrobiomemetagenomicsimmunooncologyscale simulationposterior p-value
28 stars 10.70 score 424 scripts 3 dependents
tychelab
CoSMoS:Complete Stochastic Modelling Solution
Makes univariate, multivariate, or random fields simulations precise and simple. Just select the desired time series or random fields’ properties and it will do the rest. CoSMoS is based on the framework described in Papalexiou (2018, <doi:10.1016/j.advwatres.2018.02.013>), extended for random fields in Papalexiou and Serinaldi (2020, <doi:10.1029/2019WR026331>), and further advanced in Papalexiou et al. (2021, <doi:10.1029/2020WR029466>) to allow fine-scale space-time simulation of storms (or even cyclone-mimicking fields).
Maintained by Kevin Shook. Last updated 4 years ago.
11 stars 7.10 score 77 scripts
stc04003
reReg:Recurrent Event Regression
A comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event described in Chiou et al. (2023) <doi:10.18637/jss.v105.i05>. The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) <doi:10.1198/016214501753209031>, Huang and Wang (2004) <doi:10.1198/016214504000001033>, Xu et al. (2017) <doi:10.1080/01621459.2016.1173557>, and Xu et al. (2019) <doi:10.5705/SS.202018.0224> as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event.
Maintained by Sy Han (Steven) Chiou. Last updated 3 months ago.
23 stars 6.35 score 36 scripts 1 dependents
larmarange
prevR:Estimating Regional Trends of a Prevalence from a DHS and Similar Surveys
Spatial estimation of a prevalence surface or a relative risks surface, using data from a Demographic and Health Survey (DHS) or an analog survey, see Larmarange et al. (2011) <doi:10.4000/cybergeo.24606>.
Maintained by Joseph Larmarange. Last updated 6 months ago.
5 stars 6.26 score 46 scriptsbioc
benchdamic:Benchmark of differential abundance methods on microbiome data
Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.
Maintained by Matteo Calgaro. Last updated 4 months ago.
metagenomicsmicrobiomedifferentialexpressionmultiplecomparisonnormalizationpreprocessingsoftwarebenchmarkdifferential-abundance-methods
8 stars 5.78 score 8 scripts
mvesuviusc
primerTree:Visually Assessing the Specificity and Informativeness of Primer Pairs
Identifies potential target sequences for a given set of primers and generates phylogenetic trees annotated with the taxonomies of the predicted amplification products.
Maintained by Matt Cannon. Last updated 1 years ago.
51 stars 5.56 score 16 scripts
sndmrc
BasketballAnalyzeR:Analysis and Visualization of Basketball Data
Contains data and code to accompany the book P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799.
Maintained by Marco Sandri. Last updated 2 years ago.
basketball-statsdata-analysisdata-science
35 stars 4.83 score 39 scripts
zxw834
BayesianPlatformDesignTimeTrend:Simulate and Analyse Bayesian Platform Trial with Time Trend
Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the 'rstan' package, which provides the R interface for the Stan. This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. Additionally, it allows for the study of time trend problems in platform trials. There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening. The Bayesian adaptive randomisation approaches are described in: Trippa et al. (2012) <doi:10.1200/JCO.2011.39.8420> and Wathen et al. (2017) <doi:10.1177/1740774517692302>. The randomisation algorithm is described in: Zhao W <doi:10.1016/j.cct.2015.06.008>. The analysis methods of time trend effect in platform trial are described in: Saville et al. (2022) <doi:10.1177/17407745221112013> and Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>.
Maintained by Ziyan Wang. Last updated 1 years ago.
analysisbayesian-adaptive-randomisationclinial-trialgroup-sequential-designsmultiarm-multistage-trialsplatform-trialssimulationcpp
4.38 score 12 scripts
ggloor
aIc:Testing for Compositional Pathologies in Datasets
A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <doi:10.1016/j.acags.2020.100026>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <doi:10.1007/BF00891269>) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages 'ALDEx2', 'edgeR' and 'DESeq2' (Fernandes et al (2014) <doi:10.1186/2049-2618-2-15>, Anders et al. (2013)<doi:10.1038/nprot.2013.099>).
Maintained by Greg Gloor. Last updated 1 years ago.
2 stars 4.04 score 11 scriptsbioc
omicplotR:Visual Exploration of Omic Datasets Using a Shiny App
A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata.
Maintained by Daniel Giguere. Last updated 5 months ago.
softwaredifferentialexpressiongeneexpressionguirnaseqdnaseqmetagenomicstranscriptomicsbayesianmicrobiomevisualizationsequencingimmunooncology
4.00 score 5 scripts
ybkamaleri
rreg:Visualization for Norwegian Health Quality Registries
Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.
Maintained by Yusman Kamaleri. Last updated 5 years ago.
1 stars 2.70 score 8 scripts
nilsmy
jrt:Item Response Theory Modeling and Scoring for Judgment Data
Psychometric analysis and scoring of judgment data using polytomous Item-Response Theory (IRT) models, as described in Myszkowski and Storme (2019) <doi:10.1037/aca0000225> and Myszkowski (2021) <doi:10.1037/aca0000287>. A function is used to automatically compare and select models, as well as to present a variety of model-based statistics. Plotting functions are used to present category curves, as well as information, reliability and standard error functions.
Maintained by Nils Myszkowski. Last updated 2 years ago.
2.70 score 9 scripts
cran
AlphaPart:Partition/Decomposition of Breeding Values by Paths of Information
A software that implements a method for partitioning genetic trends to quantify the sources of genetic gain in breeding programmes. The partitioning method is described in Garcia-Cortes et al. (2008) <doi:10.1017/S175173110800205X>. The package includes the main function AlphaPart for partitioning breeding values and auxiliary functions for manipulating data and summarizing, visualizing, and saving results.
Maintained by Gregor Gorjanc. Last updated 2 years ago.
2.30 scorecran
QCAtools:Helper Functions for QCA in R
Helper functions for Qualitative Comparative Analysis: evaluate and plot Boolean formulae on fuzzy set score data, apply Boolean operations, compute consistency and coverage measures.
Maintained by Jirka Lewandowski. Last updated 8 years ago.
1.70 score