W. E. Wansouwe, C. C. Kokonendji and D.T. Kolyang.
Probability mass function estimation is performed through three standard discrete associated kernels: Binomial,  Poisson and Negative binomial kernels.  The cross validation method for bandwidth selection is also implemented.

W. E. Wansouwé, C. C. Kokonendji and D. T. Kolyang
Discrete smoothing of probability mass function is performed using three discrete associated kernels: Dirac Discrete Uniform (DiracDU), Binomial and Discrete Triangular. Two automatic bandwidth selection procedures are implemented: the cross-validation method for the three kernels and the local Bayesian approach for Binomial kernel. Note that DiracDU is used for categorical data, Binomial kernel is appropriate for count data with small or moderate sample sizes, and Discrete Triangular kernel is recommended for count data with large sample sizes.

DiscML: An R package for estimating evolutionary rates of
discrete characters using maximum likelihood
DiscML performs rate estimation using maximum likelihood with the
options to correct for unobservable data, to implement a Gamma-distribution
for rate variation, and to estimate the prior root probabilities from the
empirical data.

DiscML performs maximum likelihood estimation with the options
to correct for unobservable data, to implement a Gamma-distribution for rate
variation, and to estimate the prior root probabilities from the empirical
data.

Multiple Testing Procedures with Adaptation for Discrete Tests
c(person("Sebastian", "Döhler", role = "ctb"),person("Guillermo", "Durand", role = c("aut","cre"),email = "guillermo.durand@polytechnique.org"),person("Florian","Junge", role = "aut"),person("Etienne", "Roquain", role = "ctb"))
Multiple testing procedures described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" <doi:10.1214/18-EJS1441>. The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input a set of observed p-values and their discrete support under the null. A function to compute such p-values and supports for Fisher's exact tests is also provided.
Sebastian Döhler [ctb],
Guillermo Durand [aut, cre],
Florian Junge [aut],
Etienne Roquain [ctb]