Author: Derek Beaton, Jenny Rieck, Herve Abdi
Depends: prettyGraphs (>= 2.0.0), TExPosition (>= 2.0.0), ExPosition

Description: Non-parametric resampling-based inference tests for TExPosition.
Depends: prettyGraphs (>= 2.1.4), TExPosition (>= 2.6.10), ExPosition

Packaged: 2013-12-11 15:12:39 UTC; derekbeaton

Description: Facilitate the management of data from knowledge
resources that are frequently used alone or together
In 'TKCat', knowledge resources are manipulated as modeled database (MDB)
objects. These objects provide access to the data tables along with a general
description of the resource and a detail data model documenting the
tables, their fields and their relationships.
These MDB are then gathered in catalogs that can be easily
Finally, 'TKCat' provides tools to easily subset, filter and combine MDBs and
create new catalogs suited for specific needs.
Depends: R (>= 3.6), ReDaMoR (>= 0.4.3), magrittr, dplyr, DBI,
Imports: rlang, tidyselect, visNetwork, getPass, shiny, shinydashboard,
DT, readr, jsonlite, jsonvalidate, base64enc, markdown,

Depends: R (>= 3.6), ReDaMoR (>= 0.5.2), magrittr, dplyr, DBI,
DT, htmltools, readr, jsonlite, jsonvalidate (>= 1.3.2),
base64enc, markdown, promises, future, xml2, uuid

These MDBs are then gathered in catalogs that can be easily
Depends: R (>= 3.6), ReDaMoR (>= 0.6.0), magrittr, DBI, dplyr
Imports: ClickHouseHTTP, rlang, tidyselect, visNetwork, getPass, shiny,
shinydashboard, DT, htmltools, readr, jsonlite, jsonvalidate
(>= 1.3.2), base64enc, markdown, promises, future, xml2,
Suggests: knitr, rmarkdown, stringr, RClickhouse, data.tree, BED

Depends: R (>= 3.6), ReDaMoR (>= 0.7.0), magrittr, DBI, dplyr

Title: Pairwise Distance Estimation with TKF91 and TKF92 Model
Description: Pairwise evolutionary distance estimation between protein sequences with the TKF91 and TKF92 model, which consider all the possible paths of transforming from one sequence to another.

Imports: methods, expm, numDeriv, ape (>= 3.2), phytools (>= 0.4-45),

Description: Contains functions for training and applying two-level random forest and hidden Markov models for human behavior classification from raw tri-axial accelerometer and/or GPS data. Includes functions for training a two-level model, applying the model to data, and computing performance.