Showing 10 of total 10 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 4 days ago.
86 stars 12.92 score 664 scripts 13 dependents
hojsgaard
gRbase:A Package for Graphical Modelling in R
The 'gRbase' package provides graphical modelling features used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of junction tree. 'gRbase' facilitates array operations, 'gRbase' implements functions for testing for conditional independence. 'gRbase' illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. The facilities of the package are documented in the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>) and in the paper by Dethlefsen and Højsgaard, (2005, <doi:10.18637/jss.v014.i17>). Please see 'citation("gRbase")' for citation details.
Maintained by Søren Højsgaard. Last updated 5 months ago.
3 stars 9.24 score 241 scripts 20 dependents
bioc
monocle:Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq
Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.
Maintained by Cole Trapnell. Last updated 5 months ago.
immunooncologysequencingrnaseqgeneexpressiondifferentialexpressioninfrastructuredataimportdatarepresentationvisualizationclusteringmultiplecomparisonqualitycontrolcpp
8.71 score 1.6k scripts 2 dependents
jhmaindonald
gamclass:Functions and Data for a Course on Modern Regression and Classification
Functions and data are provided that support a course that emphasizes statistical issues of inference and generalizability. The functions are designed to make it straightforward to illustrate the use of cross-validation, the training/test approach, simulation, and model-based estimates of accuracy. Methods considered are Generalized Additive Modeling, Linear and Quadratic Discriminant Analysis, Tree-based methods, and Random Forests.
Maintained by John Maindonald. Last updated 2 years ago.
4.82 score 44 scripts
cran
paleoTS:Analyze Paleontological Time-Series
Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts).
Maintained by Gene Hunt. Last updated 7 months ago.
1 stars 4.48 score 2 dependentsbioc
PDATK:Pancreatic Ductal Adenocarcinoma Tool-Kit
Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.
Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationsurvivalclusteringgeneprediction
1 stars 4.31 score 17 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 6 months ago.
2 stars 3.81 score 12 scripts 1 dependents
ncsoft
promotionImpact:Analysis & Measurement of Promotion Effectiveness
Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified variables (e.g. start of a month).
Maintained by Nahyun Kim. Last updated 5 years ago.
47 stars 3.67 score 2 scripts
fhollenbach
EBMAforecast:Estimate Ensemble Bayesian Model Averaging Forecasts using Gibbs Sampling or EM-Algorithms
Create forecasts from multiple predictions using ensemble Bayesian model averaging (EBMA). EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling. The methods in this package are Montgomery, Hollenbach, and Ward (2015) <doi:10.1016/j.ijforecast.2014.08.001> and Montgomery, Hollenbach, and Ward (2012) <doi:10.1093/pan/mps002>.
Maintained by Florian M. Hollenbach. Last updated 1 years ago.
2 stars 3.56 score 12 scripts 1 dependents
alessandromagrini
dlsem:Distributed-Lag Linear Structural Equation Models
Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models where each factor of the joint probability distribution is a distributed-lag linear regression with constrained lag shapes (Magrini, 2018 <doi:10.2478/bile-2018-0012>; Magrini et al., 2019 <doi:10.1007/s11135-019-00855-z>). DLSEMs account for temporal delays in the dependence relationships among the variables through a single parameter per covariate, thus allowing to perform dynamic causal inference in a feasible fashion. Endpoint-constrained quadratic, quadratic decreasing, linearly decreasing and gamma lag shapes are available.
Maintained by Alessandro Magrini. Last updated 5 years ago.
2.00 score 2 scripts