dplyr (>= 0.7.6), Formula, MASS (>= 7.3-50), methods, rlang
(>= 0.2.1), Rcpp, RcppGSL, RcppParallel, stats

Automatic marking of R assignments for students and teachers based
on testthat test suites.
R (>= 3.1.0), methods, yaml, testthat (>= 0.11.0), httr

Automatic marking of R assignments for students and teachers based
on 'testthat' test suites.

'assertion_funcions.R' 'expectations.R' 'mark_my_assignment.R'
'mark_my_file.R' 'markmyassignment.R' 'set_assignment.R'

methods, yaml, testthat (>= 2.0.0), httr (>= 1.0.0), checkmate
(>= 1.0.0), codetools, lazyeval, rlang

methods, yaml, testthat (>= 2.0.0), httr (>= 1.0.0), checkmate
(>= 1.0.0), codetools, rlang

Markov Chain Models for Phylogenetic Trees
Allows for fitting of maximum likelihood models using Markov chains on phylogenetic trees for analysis of discrete character data. Examples of such discrete character data include restriction sites, gene family presence/absence, intron presence/absence, and gene family size data. Hypothesis-driven user-specified substitution rate matrices can be estimated. Allows for biologically realistic models combining constrained substitution rate matrices, site rate variation, site partitioning, branch-specific rates, allowing for non-stationary prior root probabilities, correcting for sampling bias, etc.

Allows for fitting of maximum likelihood models using Markov chains
on phylogenetic trees for analysis of discrete character data. Examples of such
discrete character data include restriction sites, gene family presence/absence,
intron presence/absence, and gene family size data. Hypothesis-driven user-
specified substitution rate matrices can be estimated. Allows for biologically
realistic models combining constrained substitution rate matrices, site rate
variation, site partitioning, branch-specific rates, allowing for non-stationary
prior root probabilities, correcting for sampling bias, etc.

Allows for fitting of maximum likelihood models using Markov chains
on phylogenetic trees for analysis of discrete character data. Examples of such
discrete character data include restriction sites, gene family presence/absence,
intron presence/absence, and gene family size data. Hypothesis-driven user-
specified substitution rate matrices can be estimated. Allows for biologically
realistic models combining constrained substitution rate matrices, site rate
variation, site partitioning, branch-specific rates, allowing for non-stationary
prior root probabilities, correcting for sampling bias, etc. See Dang and Golding
(2016) <doi:10.1093/bioinformatics/btv541> for more details.