distribution functions (ECDFs), including both step
distribution functions (true ECDFs) and continuous distribution

Title: Multivariate Empirical Cumulative Distribution Functions

Description: Multivariate empirical cumulative distribution functions,
including both step functions and continuous functions.

Title: Useful Tools for Scientific Research
Authors@R: person("Tomas", "Karpati", email = "karpati@it4biotech.com",
Description: Utilities that help researchers in various phases of their research work. This package offer a function that automate the exploratory data analysis, a function for the generation of Table 1 (required on many epidemiological papers) which can be exported to excel. There is also a function for generation of a forestplot with a table and relative risks or odds ratios that can be used for publication. Additionally, there is a function that generates train/test random partitions used for model evaluation that checks for the balance of the partitions.
Imports: stats, grDevices, rlang, utils, car, Hmisc, ROSE, pROC,
ResourceSelection, InformationValue, gmodels, dplyr, tibble,
Author: Tomas Karpati [aut, cre] (<https://orcid.org/0000-0003-2650-6192>)

Title: Compare Microbial Networks of 'trans_network' Class of
Authors@R: c(person("Chi", "Liu", role = c("aut", "cre"), email = "liuchi0426@126.com"),
person("Minjie", "Yao", role = "ctb", email = "yaomj@fafu.edu.cn"),
person("Xiangzhen", "Li", role = "ctb", email = "lixz@cib.ac.cn")
Description: Compare microbial co-occurrence networks created from 'trans_network' class of 'microeco' package <https://github.com/ChiLiubio/microeco>.
This package is the extension of 'trans_network' class of 'microeco' package and especially useful when different networks are constructed and analyzed simultaneously.
Imports: microeco, magrittr, dplyr, igraph, reshape2

Imports: R6, microeco (>= 0.12.0), magrittr, dplyr, igraph, reshape2,
Suggests: rgexf, ape, file2meco, agricolae

Title: Measurement Error Correction in Linear Models with a Continuous
Description: Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) <doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) <doi:10.1002/sim.3713>. Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) <doi:10.1002/sim.7011>. Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and Rosner B, Spiegelman D & Willett WC (1992) <doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).