Showing 11 of total 11 results (show query)
michaelhallquist
MplusAutomation:An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus
Leverages the R language to automate latent variable model estimation and interpretation using 'Mplus', a powerful latent variable modeling program developed by Muthen and Muthen (<https://www.statmodel.com>). Specifically, this package provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics.
Maintained by Michael Hallquist. Last updated 5 days ago.
86 stars 12.92 score 664 scripts 13 dependents
bluefoxr
COINr:Composite Indicator Construction and Analysis
A comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.
Maintained by William Becker. Last updated 2 months ago.
26 stars 8.94 score 73 scripts 1 dependents
genentech
psborrow2:Bayesian Dynamic Borrowing Analysis and Simulation
Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <https://stan-dev.r-universe.dev>.
Maintained by Matt Secrest. Last updated 2 months ago.
bayesian-dynamic-borrowingpsborrow2simulation-study
18 stars 7.87 score 16 scripts
bioc
DEP:Differential Enrichment analysis of Proteomics data
This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package.
Maintained by Arne Smits. Last updated 5 months ago.
immunooncologyproteomicsmassspectrometrydifferentialexpressiondatarepresentation
7.10 score 628 scripts
appsilon
data.validator:Automatic Data Validation and Reporting
Validate dataset by columns and rows using convenient predicates inspired by 'assertr' package. Generate good looking HTML report or print console output to display in logs of your data processing pipeline.
Maintained by Marcin Dubel. Last updated 12 months ago.
datareportingrhinoverserstudiovalidation
147 stars 6.67 score 40 scripts
sqyu
genscore:Generalized Score Matching Estimators
Implementation of the Generalized Score Matching estimator in Yu et al. (2019) <http://jmlr.org/papers/v20/18-278.html> for non-negative graphical models (truncated Gaussian, exponential square-root, gamma, a-b models) and univariate truncated Gaussian distributions. Also includes the original estimator for untruncated Gaussian graphical models from Lin et al. (2016) <doi:10.1214/16-EJS1126>, with the addition of a diagonal multiplier.
Maintained by Shiqing Yu. Last updated 5 years ago.
density-estimationgraphical-modelsinteraction-modelsscore-matchingundirected-graphs
1 stars 4.18 score 3 scripts 1 dependents
rjdverse
rjd3nowcasting:Nowcasting with 'JDemetra+ 3.0'
Interface around 'JDemetra+ 3.x' (<https://github.com/jdemetra/jdplus-nowcasting>), TSACE project. It defines and estimates Dynamic Factor Models with the purpose of Nowcasting. News analysis is included in this second version.
Maintained by Corentin Lemasson. Last updated 9 months ago.
3 stars 4.13 score 1 scripts
lbau7
basksim:Simulation-Based Calculation of Basket Trial Operating Characteristics
Provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988>, Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>, Berry et al. (2020) <doi:10.1177/1740774513497539>, Neuenschwander et al. (2016) <doi:10.1002/pst.1730> and Psioda et al. (2021) <doi:10.1093/biostatistics/kxz014>. For the latter three designs, the functions are mostly wrappers for functions provided by the packages 'bhmbasket' and 'bmabasket'.
Maintained by Lukas Baumann. Last updated 12 months ago.
1 stars 3.28 score 19 scripts
kwb-r
kwb.swmm:R Package with Functions for Working with EPA`s Storm Water Management Model (SWMM)
R package with functions for working with EPA`s Storm Water Management Model [SWMM](https://www.epa.gov/water-research/storm-water-management-model-swmm).
Maintained by Michael Rustler. Last updated 1 years ago.
project-keysstormwater-management-modelswmm
3.18 score 2 scripts 1 dependents
pieterprovoost
ghettoblaster:NCBI BLAST web client
NCBI BLAST web client.
Maintained by Pieter Provoost. Last updated 1 years ago.
1.70 score
