Showing 200 of total 2081 results (show query)

r-lib

devtools:Tools to Make Developing R Packages Easier

Collection of package development tools.

Maintained by Jennifer Bryan. Last updated 6 months ago.

package-creation

2.4k stars 19.55 score 51k scripts 150 dependents

quarto-dev

quarto:R Interface to 'Quarto' Markdown Publishing System

Convert R Markdown documents and 'Jupyter' notebooks to a variety of output formats using 'Quarto'.

Maintained by Christophe Dervieux. Last updated 12 days ago.

147 stars 14.98 score 1.3k scripts 36 dependents

functionaldata

fdapace:Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Maintained by Yidong Zhou. Last updated 9 months ago.

cpp

31 stars 11.54 score 474 scripts 25 dependents

sachaepskamp

semPlot:Path Diagrams and Visual Analysis of Various SEM Packages' Output

Path diagrams and visual analysis of various SEM packages' output.

Maintained by Sacha Epskamp. Last updated 3 years ago.

63 stars 10.64 score 2.1k scripts 13 dependents

milesmcbain

datapasta:R Tools for Data Copy-Pasta

RStudio addins and R functions that make copy-pasting vectors and tables to text painless.

Maintained by Miles McBain. Last updated 3 years ago.

addinclipboardcopypasteexceltibble

899 stars 10.32 score 290 scripts 2 dependents

john-d-fox

Rcmdr:R Commander

A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.

Maintained by John Fox. Last updated 5 months ago.

4 stars 9.48 score 636 scripts 38 dependents

briencj

asremlPlus:Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences

Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' <https://vsni.co.uk/> as 'asreml-R', who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 1 months ago.

asremlmixed-models

19 stars 9.37 score 200 scripts

guangchuangyu

badger:Badge for R Package

Query information and generate badge for using in README and GitHub Pages.

Maintained by Guangchuang Yu. Last updated 9 months ago.

badge

197 stars 8.92 score 225 scripts 5 dependents

pik-piam

remind2:The REMIND R package (2nd generation)

Contains the REMIND-specific routines for data and model output manipulation.

Maintained by Renato Rodrigues. Last updated 2 days ago.

8.87 score 161 scripts 5 dependents

brockk

escalation:A Modular Approach to Dose-Finding Clinical Trials

Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients.

Maintained by Kristian Brock. Last updated 1 days ago.

15 stars 8.16 score 67 scripts

yonicd

ggedit:Interactive 'ggplot2' Layer and Theme Aesthetic Editor

Interactively edit 'ggplot2' layer and theme aesthetics definitions.

Maintained by Jonathan Sidi. Last updated 11 months ago.

ggplot2shiny

250 stars 7.95 score 116 scripts 3 dependents

pik-piam

magpie4:MAgPIE outputs R package for MAgPIE version 4.x

Common output routines for extracting results from the MAgPIE framework (versions 4.x).

Maintained by Benjamin Leon Bodirsky. Last updated 2 days ago.

2 stars 7.89 score 254 scripts 9 dependents