Showing 200 of total 395 results (show query)

edzer

hexbin:Hexagonal Binning Routines

Binning and plotting functions for hexagonal bins.

Maintained by Edzer Pebesma. Last updated 5 months ago.

fortran

37 stars 14.00 score 2.4k scripts 114 dependents

brodieg

diffobj:Diffs for R Objects

Generate a colorized diff of two R objects for an intuitive visualization of their differences.

Maintained by Brodie Gaslam. Last updated 3 years ago.

diff

231 stars 13.17 score 107 scripts 494 dependents

miraisolutions

XLConnect:Excel Connector for R

Provides comprehensive functionality to read, write and format Excel data.

Maintained by Martin Studer. Last updated 30 days ago.

cross-platformexcelr-languagexlconnectopenjdk

130 stars 12.28 score 1.2k scripts 1 dependents

mclements

rstpm2:Smooth Survival Models, Including Generalized Survival Models

R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.

Maintained by Mark Clements. Last updated 5 months ago.

fortranopenblascpp

27 stars 11.09 score 137 scripts 52 dependents

bpfaff

urca:Unit Root and Cointegration Tests for Time Series Data

Unit root and cointegration tests encountered in applied econometric analysis are implemented.

Maintained by Bernhard Pfaff. Last updated 10 months ago.

fortran

6 stars 8.95 score 1.4k scripts 270 dependents

bioc

Category:Category Analysis

A collection of tools for performing category (gene set enrichment) analysis.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

annotationgopathwaysgenesetenrichment

7.93 score 183 scripts 16 dependents

dankelley

plan:Tools for Project Planning

Supports the creation of 'burndown' charts and 'gantt' diagrams.

Maintained by Dan Kelley. Last updated 2 years ago.

33 stars 7.23 score 103 scripts

roustant

DiceKriging:Kriging Methods for Computer Experiments

Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.

Maintained by Olivier Roustant. Last updated 4 years ago.

4 stars 6.99 score 526 scripts 37 dependents

cran

fGarch:Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

Analyze and model heteroskedastic behavior in financial time series.

Maintained by Georgi N. Boshnakov. Last updated 1 years ago.

fortran

7 stars 6.33 score 51 dependents

bioc

qusage:qusage: Quantitative Set Analysis for Gene Expression

This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu)

Maintained by Christopher Bolen. Last updated 5 months ago.

genesetenrichmentmicroarrayrnaseqsoftwareimmunooncology

5.65 score 185 scripts 1 dependents

staffanbetner

rethinking:Statistical Rethinking book package

Utilities for fitting and comparing models

Maintained by Richard McElreath. Last updated 4 months ago.

5.42 score 4.4k scripts

simonmoulds

lulcc:Land Use Change Modelling in R

Classes and methods for spatially explicit land use change modelling in R.

Maintained by Simon Moulds. Last updated 5 years ago.

41 stars 5.37 score 38 scripts

r-forge

R2MLwiN:Running 'MLwiN' from Within R

An R command interface to the 'MLwiN' multilevel modelling software package.

Maintained by Zhengzheng Zhang. Last updated 9 days ago.

5.35 score 125 scripts