Showing 13 of total 13 results (show query)
amices
mice:Multivariate Imputation by Chained Equations
Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
Maintained by Stef van Buuren. Last updated 1 days ago.
chained-equationsfcsimputationmicemissing-datamissing-valuesmultiple-imputationmultivariate-datacpp
462 stars 16.64 score 10k scripts 154 dependents
reconhub
incidence:Compute, Handle, Plot and Model Incidence of Dated Events
Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to 'incidence' objects using 'fit'. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Maintained by Tim Taylor. Last updated 8 months ago.
58 stars 12.06 score 504 scripts 11 dependents
bioc
methylKit:DNA methylation analysis from high-throughput bisulfite sequencing results
methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files.
Maintained by Altuna Akalin. Last updated 29 days ago.
dnamethylationsequencingmethylseqgenome-biologymethylationstatistical-analysisvisualizationcurlbzip2xz-utilszlibcpp
220 stars 11.80 score 578 scripts 3 dependents
spatstat
spatstat.explore:Exploratory Data Analysis for the 'spatstat' Family
Functionality for exploratory data analysis and nonparametric analysis of 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'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Maintained by Adrian Baddeley. Last updated 10 days ago.
cluster-detectionconfidence-intervalshypothesis-testingk-functionroc-curvesscan-statisticssignificance-testingsimulation-envelopesspatial-analysisspatial-data-analysisspatial-sharpeningspatial-smoothingspatial-statistics
1 stars 10.18 score 67 scripts 150 dependentsbioc
snpStats:SnpMatrix and XSnpMatrix classes and methods
Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes.
Maintained by David Clayton. Last updated 2 days ago.
microarraysnpgeneticvariabilityzlib
9.68 score 674 scripts 23 dependents
insightsengineering
rbmi:Reference Based Multiple Imputation
Implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular, this package supports deterministic conditional mean imputation and jackknifing as described in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.
Maintained by Isaac Gravestock. Last updated 1 months ago.
18 stars 8.76 score 33 scripts 1 dependents
bgoodri
mi:Missing Data Imputation and Model Checking
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
Maintained by Ben Goodrich. Last updated 3 years ago.
2 stars 8.25 score 244 scripts 47 dependentsbioc
nullranges:Generation of null ranges via bootstrapping or covariate matching
Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package.
Maintained by Michael Love. Last updated 5 months ago.
visualizationgenesetenrichmentfunctionalgenomicsepigeneticsgeneregulationgenetargetgenomeannotationannotationgenomewideassociationhistonemodificationchipseqatacseqdnaseseqrnaseqhiddenmarkovmodelbioconductorbootstrapgenomicsmatchingstatistics
27 stars 8.16 score 50 scripts 1 dependents
farhadpishgar
MatchThem:Matching and Weighting Multiply Imputed Datasets
Provides essential tools for the pre-processing techniques of matching and weighting multiply imputed datasets. The package includes functions for matching within and across multiply imputed datasets using various methods, estimating weights for units in the imputed datasets using multiple weighting methods, calculating causal effect estimates in each matched or weighted dataset using parametric or non-parametric statistical models, and pooling the resulting estimates according to Rubin's rules (please see <https://journal.r-project.org/archive/2021/RJ-2021-073/> for more details).
Maintained by Farhad Pishgar. Last updated 5 months ago.
18 stars 7.40 score 112 scripts
maarten14c
rice:Radiocarbon Equations
Provides functions for the calibration of radiocarbon dates, as well as options to calculate different radiocarbon realms (C14 age, F14C, pMC, D14C) and estimating the effects of contamination or local reservoir offsets (Reimer and Reimer 2001 <doi:10.1017/S0033822200038339>). The methods follow long-established recommendations such as Stuiver and Polach (1977) <doi:10.1017/S0033822200003672> and Reimer et al. (2004) <doi:10.1017/S0033822200033154>. This package complements the data package 'rintcal'.
Maintained by Maarten Blaauw. Last updated 3 months ago.
1 stars 6.13 score 13 scripts 4 dependents
bergsmat
nonmemica:Create and Evaluate NONMEM Models in a Project Context
Systematically creates and modifies NONMEM(R) control streams. Harvests NONMEM output, builds run logs, creates derivative data, generates diagnostics. NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for nonlinear mixed effects modeling. See 'package?nonmemica'.
Maintained by Tim Bergsma. Last updated 3 months ago.
4 stars 4.58 score 45 scripts
cran
benthos:Marine Benthic Ecosystem Analysis
Preprocessing tools and biodiversity measures (species abundance, species richness, population heterogeneity and sensitivity) for analysing marine benthic data. See Van Loon et al. (2015) <doi:10.1016/j.seares.2015.05.002> for an application of these tools.
Maintained by Dennis Walvoort. Last updated 3 years ago.
2.00 score