Showing 14 of total 14 results (show query)
bioc
pcaMethods:A collection of PCA methods
Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany.
Maintained by Henning Redestig. Last updated 5 months ago.
49 stars 13.10 score 538 scripts 73 dependents
spatstat
spatstat.model:Parametric Statistical Modelling and Inference for the 'spatstat' Family
Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.
Maintained by Adrian Baddeley. Last updated 8 days ago.
analysis-of-variancecluster-processconfidence-intervalscox-processdeterminantal-point-processesgibbs-processinfluenceleveragemodel-diagnosticsneyman-scottparameter-estimationpoisson-processspatial-analysisspatial-modellingspatial-point-processesstatistical-inference
5 stars 9.09 score 6 scripts 46 dependents
aloy
HLMdiag:Diagnostic Tools for Hierarchical (Multilevel) Linear Models
A suite of diagnostic tools for hierarchical (multilevel) linear models. The tools include not only leverage and traditional deletion diagnostics (Cook's distance, covratio, covtrace, and MDFFITS) but also convenience functions and graphics for residual analysis. Models can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package.
Maintained by Adam Loy. Last updated 4 years ago.
17 stars 8.63 score 170 scripts 7 dependents
rfastofficial
Rfast2:A Collection of Efficient and Extremely Fast R Functions II
A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.
Maintained by Manos Papadakis. Last updated 1 years ago.
38 stars 8.09 score 75 scripts 26 dependents
alexchristensen
NetworkToolbox:Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis
Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.
Maintained by Alexander Christensen. Last updated 2 years ago.
23 stars 7.04 score 101 scripts 4 dependents
lotze
COMPoissonReg:Conway-Maxwell Poisson (COM-Poisson) Regression
Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The package provides functions for model estimation, dispersion testing, and diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016) <doi:10.1016/j.csda.2016.01.007> is also supported.
Maintained by Andrew Raim. Last updated 1 years ago.
9 stars 6.63 score 53 scripts 3 dependents
josie-athens
pubh:A Toolbox for Public Health and Epidemiology
A toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. Includes a function to report coefficients and confidence intervals from models using robust standard errors (when available), functions that expand 'ggplot2' plots and functions relevant for introductory papers in Epidemiology or Public Health. Please note that use of the provided data sets is for educational purposes only.
Maintained by Josie Athens. Last updated 6 months ago.
5 stars 5.73 score 72 scripts
mandymejia
fMRIscrub:Scrubbing and Other Data Cleaning Routines for fMRI
Data-driven fMRI denoising with projection scrubbing (Pham et al (2022) <doi:10.1016/j.neuroimage.2023.119972>). Also includes routines for DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) <doi:10.1016/j.neuroimage.2017.12.098>), motion scrubbing (Power et al (2012) <doi:10.1016/j.neuroimage.2011.10.018>), aCompCor (anatomical Components Correction) (Muschelli et al (2014) <doi:10.1016/j.neuroimage.2014.03.028>), detrending, and nuisance regression. Projection scrubbing is also applicable to other outlier detection tasks involving high-dimensional data.
Maintained by Amanda Mejia. Last updated 1 days ago.
4 stars 4.56 score 15 scripts 1 dependentsbioc
lmdme:Linear Model decomposition for Designed Multivariate Experiments
linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS.
Maintained by Cristobal Fresno. Last updated 5 months ago.
microarrayonechanneltwochannelvisualizationdifferentialexpressionexperimentdatacancer
3.78 score 1 scripts
lhvanegasp
glmtoolbox:Set of Tools to Data Analysis using Generalized Linear Models
Set of tools for the statistical analysis of data using: (1) normal linear models; (2) generalized linear models; (3) negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion; (4) beta-binomial and random-clumped binomial regression models as alternative to the binomial regression models under the presence of overdispersion; (5) Zero-inflated and zero-altered regression models to deal with zero-excess in count data; (6) generalized nonlinear models; (7) generalized estimating equations for cluster correlated data.
Maintained by Luis Hernando Vanegas. Last updated 8 months ago.
1 stars 3.10 score 149 scripts
wernerstahel
plgraphics:User Oriented Plotting Functions
Plots with high flexibility and easy handling, including informative regression diagnostics for many models.
Maintained by Werner A. Stahel. Last updated 2 years ago.
2.08 score 12 scripts
cran
centiserve:Find Graph Centrality Indices
Calculates centrality indices additional to the 'igraph' package centrality functions.
Maintained by Mahdi Jalili. Last updated 8 years ago.
1 stars 2.08 score 1 dependents
daphnaharel
sur:Companion to "Statistics Using R: An Integrative Approach"
Access to the datasets and many of the functions used in "Statistics Using R: An Integrative Approach". These datasets include a subset of the National Education Longitudinal Study, the Framingham Heart Study, as well as several simulated datasets used in the examples throughout the textbook. The functions included in the package reproduce some of the functionality of 'Stata' that is not directly available in 'R'. The package also contains a tutorial on basic data frame management, including how to handle missing data.
Maintained by Daphna Harel. Last updated 5 years ago.
1.26 score 18 scripts
