Suggests: testthat (>= 3.0.0), rvest, devtools, covr, rmarkdown, knitr

Description: Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.
Depends: R (>= 3.3.0), Iso, stats, truncnorm, imputeLCMD

Depends: R (>= 3.3.0), Iso, stats, truncnorm, norm

Depends: R (>= 3.3.0), Iso, stats, truncnorm, norm, missForest, missMDA

Title: Quantification of Population-Level Impact of Vaccination
Implements the compartment model from Tokars (2018)
<doi:10.1016/j.vaccine.2018.10.026>. This enables quantification of
population-wide impact of vaccination against vaccine-preventable
Imports: Rcpp, tibble, dplyr, rlang, glue, lubridate, magrittr

Title: Mechanical Loading Prediction Through Accelerometer Data
Description: Functions to read, process and analyse accelerometer
data related to mechanical loading variables. This package is
developed and tested for use with raw accelerometer data from
Imports: glue, lubridate, lvmisc, pillar, pracma, purrr, Rcpp, rlang
(>= 0.4.6), signal, stringr, tibble, toOrdinal, utils, vroom
Packaged: 2021-06-30 09:37:42 UTC; lucasveras
Author: Lucas Veras [aut, cre] (<https://orcid.org/0000-0003-0562-5803>)

Packaged: 2021-09-03 06:08:26 UTC; lucasveras

Packaged: 2021-11-18 10:50:53 UTC; lucasveras

Suggests: accdata, covr, knitr, rmarkdown, testthat (>= 3.0.0)
Packaged: 2022-01-15 20:37:09 UTC; lucasveras

Packaged: 2022-08-08 08:27:24 UTC; lucasveras

Title: Imprecise Imputation for Statistical Matching
Description: Imputing blockwise missing data by imprecise imputation,
featuring a domain-based, variable-wise, and case-wise strategy.
Furthermore, the estimation of lower and upper bounds for
unconditional and conditional probabilities based on the obtained
Additionally, two utility functions are supplied: one to check
whether variables in a data set contain set-valued observations;
and another to merge two already imprecisely imputed data.

The method is described in a technical report by Endres, Fink and

Title: Array Operations for Arrays of Mismatching Sizes
Description: Support for implicit expansion of arrays in operations involving
arrays of mismatching sizes. This pattern is known as "broadcasting" in
'Python' and  "implicit expansion" in 'Matlab' and is explained for example in
the article "Array programming with NumPy" by C. R. Harris et al. (2020)