Showing 78 of total 78 results (show query)

andyliaw-mrk

locfit:Local Regression, Likelihood and Density Estimation

Local regression, likelihood and density estimation methods as described in the 1999 book by Loader.

Maintained by Andy Liaw. Last updated 11 days ago.

3.3 match 1 stars 9.40 score 428 scripts 606 dependents

wraff

wrMisc:Analyze Experimental High-Throughput (Omics) Data

The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too.

Maintained by Wolfgang Raffelsberger. Last updated 7 months ago.

6.8 match 4.44 score 33 scripts 4 dependents

aiorazabala

qmethod:Analysis of Subjective Perspectives Using Q Methodology

Analysis of Q methodology, used to identify distinct perspectives existing within a group. This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see <https://qmethod.org/>). A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors. The package allows to choose either principal components or centroid factor extraction, manual or automatic flagging, a number of mathematical methods for rotation (or none), and a number of correlation coefficients for the initial correlation matrix, among many other options. Additional functions are available to import and export data (from raw *.CSV, 'HTMLQ' and 'FlashQ' *.CSV, 'PQMethod' *.DAT and 'easy-htmlq' *.JSON files), to print and plot, to import raw data from individual *.CSV files, and to make printable cards. The package also offers functions to print Q cards and to generate Q distributions for study administration. See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a graphical user interface (GUI) for online and offline use.

Maintained by Aiora Zabala. Last updated 1 years ago.

4.0 match 38 stars 6.03 score 47 scripts

sprfmo

jjmR:Graphics and diagnostics tools for SPRFMO's Joint Jack Mackerel model

Graphics and diagnostics tools for SPRFMO's Joint Jack Mackerel model.

Maintained by Ricardo Oliveros-Ramos. Last updated 5 months ago.

3.4 match 2 stars 3.81 score 12 scripts 1 dependents

repboxr

repboxReg:Repbox module for analysing regressions

Repbox module for analysing regressions

Maintained by Sebastian Kranz. Last updated 30 days ago.

3.4 match 3.71 score 6 scripts 2 dependents

bioc

omicsViewer:Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer

omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisherโ€™s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript.

Maintained by Chen Meng. Last updated 2 months ago.

softwarevisualizationgenesetenrichmentdifferentialexpressionmotifdiscoverynetworknetworkenrichment

1.8 match 4 stars 6.02 score 22 scripts

skranz

rgmpl:Helps solving in R linear programming models specified in GMPL

Tools that help to solve linear programming models specified in GMPL

Maintained by Sebastian Kranz. Last updated 3 years ago.

gmpl

3.7 match 2.18 score 1 scripts 1 dependents

skranz

rampl:Tools to work with AMPL from R

Tools to work with AMPL from R. Writing AMPL dat files, running AMPL locally or using the NEOS solvers.

Maintained by Sebastian Kranz. Last updated 4 years ago.

2.4 match 2.16 score 29 scripts