Showing 116 of total 116 results (show query)

henrikbengtsson

R.utils:Various Programming Utilities

Utility functions useful when programming and developing R packages.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

3.3 match 63 stars 13.74 score 5.7k scripts 814 dependents

gaborcsardi

dotenv:Load Environment Variables from '.env'

Load configuration from a '.env' file, that is in the current working directory, into environment variables.

Maintained by Gábor Csárdi. Last updated 2 years ago.

5.3 match 93 stars 8.55 score 884 scripts 2 dependents

tidyverse

dplyr:A Grammar of Data Manipulation

A fast, consistent tool for working with data frame like objects, both in memory and out of memory.

Maintained by Hadley Wickham. Last updated 13 days ago.

data-manipulationgrammarcpp

1.3 match 4.8k stars 24.68 score 659k scripts 7.8k dependents

sagiegurari

scriptexec:Execute Native Scripts

Run complex native scripts with a single command, similar to system commands.

Maintained by Sagie Gur-Ari. Last updated 4 years ago.

bash-scriptscriptingshell-script

3.4 match 3 stars 4.50 score 21 scripts

bioc

widgetTools:Creates an interactive tcltk widget

This packages contains tools to support the construction of tcltk widgets

Maintained by Jianhua Zhang. Last updated 5 months ago.

infrastructure

3.0 match 5.04 score 11 scripts 8 dependents

skranz

skUtils:Helper functions for repgames and dyngames

Helper functions needed by my package repgames and dyngames

Maintained by Sebastian Kranz. Last updated 4 years ago.

cpp

8.5 match 1.00 score

repboxr

repboxReg:Repbox module for analysing regressions

Repbox module for analysing regressions

Maintained by Sebastian Kranz. Last updated 30 days ago.

1.9 match 3.71 score 6 scripts 2 dependents

repboxr

repboxR:Repbox for R code files in code supplements

Repbox for R code files in code supplements

Maintained by Sebastian Kranz. Last updated 1 years ago.

2.3 match 2.48 score 2 dependents

the-mad-statter

wubik:Helpful R Functions for Databricks at WashU

This package provides helpful functions for using R on Databricks at WashU.

Maintained by Matthew Schuelke. Last updated 9 months ago.

3.0 match 1.70 score 1 scripts

repboxr

repboxRfun:Repbox functions called by code injections into R scripts

Try to use as little special dependencies as reasonably possible

Maintained by Sebastian Kranz. Last updated 1 years ago.

2.0 match 2.18 score 1 dependents

piotrekjanus

aiRly:R Wrapper for 'Airly' API

Get information about air quality using 'Airly' <https://airly.eu/> API through R.

Maintained by Piotr Janus. Last updated 5 years ago.

1.6 match 2.70 score 2 scripts

ecohealthalliance

ehallm:What the Package Does (Title Case)

More about what it does (maybe more than one line) Use four spaces when indenting paragraphs within the Description.

Maintained by The package maintainer. Last updated 4 months ago.

1.6 match 1 stars 2.18 score

vmoprojs

GeoModels:Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis

Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.

Maintained by Moreno Bevilacqua. Last updated 2 months ago.

fortranopenblasglibc

0.5 match 3 stars 4.17 score 83 scripts

nie-xiuquan

EasyDescribe:A Convenient Way of Descriptive Statistics

Descriptive Statistics is essential for publishing articles. This package can perform descriptive statistics according to different data types. If the data is a continuous variable, the mean and standard deviation or median and quartiles are automatically output; if the data is a categorical variable, the number and percentage are automatically output. In addition, if you enter two variables in this package, the two variables will be described and their relationships will be tested automatically according to their data types. For example, if one of the two input variables is a categorical variable, another variable will be described hierarchically based on the categorical variable and the statistical differences between different groups will be compared using appropriate statistical methods. And for groups of more than two, the post hoc test will be applied. For more information on the methods we used, please see the following references: Libiseller, C. and Grimvall, A. (2002) <doi:10.1002/env.507>, Patefield, W. M. (1981) <doi:10.2307/2346669>, Hope, A. C. A. (1968) <doi:10.1111/J.2517-6161.1968.TB00759.X>, Mehta, C. R. and Patel, N. R. (1983) <doi:10.1080/01621459.1983.10477989>, Mehta, C. R. and Patel, N. R. (1986) <doi:10.1145/6497.214326>, Clarkson, D. B., Fan, Y. and Joe, H. (1993) <doi:10.1145/168173.168412>, Cochran, W. G. (1954) <doi:10.2307/3001616>, Armitage, P. (1955) <doi:10.2307/3001775>, Szabo, A. (2016) <doi:10.1080/00031305.2017.1407823>, David, F. B. (1972) <doi:10.1080/01621459.1972.10481279>, Joanes, D. N. and Gill, C. A. (1998) <doi:10.1111/1467-9884.00122>, Dunn, O. J. (1964) <doi:10.1080/00401706.1964.10490181>, Copenhaver, M. D. and Holland, B. S. (1988) <doi:10.1080/00949658808811082>, Chambers, J. M., Freeny, A. and Heiberger, R. M. (1992) <doi:10.1201/9780203738535-5>, Shaffer, J. P. (1995) <doi:10.1146/annurev.ps.46.020195.003021>, Myles, H. and Douglas, A. W. (1973) <doi:10.2307/2063815>, Rahman, M. and Tiwari, R. (2012) <doi:10.4236/health.2012.410139>, Thode, H. J. (2002) <doi:10.1201/9780203910894>, Jonckheere, A. R. (1954) <doi:10.2307/2333011>, Terpstra, T. J. (1952) <doi:10.1016/S1385-7258(52)50043-X>.

Maintained by Xiuquan Nie. Last updated 2 years ago.

0.5 match 1 stars 2.48 score