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henrikbengtsson

R.utils:Various Programming Utilities

Utility functions useful when programming and developing R packages.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

45.7 match 63 stars 13.74 score 5.7k scripts 814 dependents

yulab-smu

yulab.utils:Supporting Functions for Packages Maintained by 'YuLab-SMU'

Miscellaneous functions commonly used by 'YuLab-SMU'.

Maintained by Guangchuang Yu. Last updated 4 days ago.

37.5 match 33 stars 9.94 score 21 scripts 228 dependents

nepem-ufsc

metan:Multi Environment Trials Analysis

Performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric adaptability index by Mohammadi & Amri (2008) <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>, stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>, weighted average of absolute scores by Olivoto et al. (2019a) <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>. Non-parametric methods includes superiority index by Lin & Binns (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic stability by Huehn (1990) <doi:10.1007/BF00024241>, TOP third statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided.

Maintained by Tiago Olivoto. Last updated 10 days ago.

17.0 match 2 stars 9.48 score 1.3k scripts 2 dependents

rmi-pacta

pacta.portfolio.utils:pacta.portfolio.utils

For more information visit <https://rmi.org/>.

Maintained by CJ Yetman. Last updated 3 months ago.

climate-changepactapactaversesustainable-finance

36.7 match 1 stars 4.13 score 5 scripts 3 dependents

efinite

utile.tools:Summarize Data for Publication

Tools for formatting and summarizing data for outcomes research.

Maintained by Eric Finnesgard. Last updated 2 years ago.

37.5 match 5 stars 3.99 score 13 scripts 1 dependents

efinite

utile.visuals:Create Visuals for Publication

A set of functions to aid in the production of visuals in ggplot2.

Maintained by Eric Finnesgard. Last updated 2 years ago.

37.5 match 4 stars 3.30 score 7 scripts

efinite

utile.tables:Build Tables for Publication

Functions for building customized ready-to-export tables for publication.

Maintained by Eric Finnesgard. Last updated 2 years ago.

37.5 match 1 stars 2.70 score 2 scripts

skranz

gtree:gtree basic functionality to model and solve games

gtree basic functionality to model and solve games

Maintained by Sebastian Kranz. Last updated 4 years ago.

economic-experimentseconomicsgambitgame-theorynash-equilibrium

26.4 match 18 stars 3.79 score 23 scripts 1 dependents

mrc-ide

moz.utils:Utility functions

Utility functions, for useful utilitarian uses.

Maintained by Oli Stevens. Last updated 2 months ago.

41.9 match 2.26 score 18 scripts

neurodata

lolR:Linear Optimal Low-Rank Projection

Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package and the associated manuscript Vogelstein et al. (2017) <arXiv:1709.01233>, we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications.

Maintained by Eric Bridgeford. Last updated 4 years ago.

8.2 match 20 stars 7.28 score 80 scripts

laplacesdemonr

LaplacesDemon:Complete Environment for Bayesian Inference

Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).

Maintained by Henrik Singmann. Last updated 12 months ago.

4.0 match 93 stars 13.45 score 1.8k scripts 60 dependents

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

3.6 match 15 stars 12.60 score 7.7k scripts 295 dependents

insightsengineering

tern:Create Common TLGs Used in Clinical Trials

Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.

Maintained by Joe Zhu. Last updated 2 months ago.

clinical-trialsgraphslistingsnestoutputstables

3.5 match 79 stars 12.62 score 186 scripts 9 dependents

fmichonneau

phylobase:Base Package for Phylogenetic Structures and Comparative Data

Provides a base S4 class for comparative methods, incorporating one or more trees and trait data.

Maintained by Francois Michonneau. Last updated 1 years ago.

phylogeneticscpp

3.9 match 18 stars 11.14 score 394 scripts 18 dependents

emf-creaf

meteoland:Landscape Meteorology Tools

Functions to estimate weather variables at any position of a landscape [De Caceres et al. (2018) <doi:10.1016/j.envsoft.2018.08.003>].

Maintained by Miquel De Cáceres. Last updated 2 months ago.

cpp

5.4 match 10 stars 7.95 score 92 scripts 2 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

3.9 match 28 stars 11.01 score 137 scripts 50 dependents

braverock

PortfolioAnalytics:Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios

Portfolio optimization and analysis routines and graphics.

Maintained by Brian G. Peterson. Last updated 3 months ago.

3.6 match 81 stars 11.49 score 626 scripts 2 dependents

jimbrig

jimstools:Tools for R

What the package does (one paragraph).

Maintained by Jimmy Briggs. Last updated 3 years ago.

functionspersonalutility

13.9 match 2 stars 3.00 score 2 scripts

rstudio

rmarkdown:Dynamic Documents for R

Convert R Markdown documents into a variety of formats.

Maintained by Yihui Xie. Last updated 4 months ago.

literate-programmingmarkdownpandocrmarkdown

1.9 match 2.9k stars 21.79 score 14k scripts 3.7k dependents

yanlinlin82

ggvenn:Draw Venn Diagram by 'ggplot2'

An easy-to-use way to draw pretty venn diagram by 'ggplot2'.

Maintained by Linlin Yan. Last updated 1 months ago.

easy-to-useggplot2venn-diagram

3.6 match 170 stars 11.34 score 1.8k scripts 8 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 2 months ago.

5.0 match 15 stars 7.91 score 67 scripts