Showing 7 of total 7 results (show query)
ruthkr
greatR:Gene Registration from Expression and Time-Courses in R
A tool for registering (aligning) gene expression profiles between reference and query data.
Maintained by Ruth Kristianingsih. Last updated 9 months ago.
1 stars 5.12 score 11 scripts
cran
optimsimplex:R Port of the 'Scilab' Optimsimplex Module
Provides a building block for optimization algorithms based on a simplex. The 'optimsimplex' package may be used in the following optimization methods: the simplex method of Spendley et al. (1962) <doi:10.1080/00401706.1962.10490033>, the method of Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>, Box's algorithm for constrained optimization (1965) <doi:10.1093/comjnl/8.1.42>, the multi-dimensional search by Torczon (1989) <https://www.cs.wm.edu/~va/research/thesis.pdf>, etc...
Maintained by Sebastien Bihorel. Last updated 3 years ago.
3.65 score 3 dependentscran
scaRabee:Optimization Toolkit for Pharmacokinetic-Pharmacodynamic Models
A port of the Scarabee toolkit originally written as a Matlab-based application. scaRabee provides a framework for simulation and optimization of pharmacokinetic-pharmacodynamic models at the individual and population level. It is built on top of the neldermead package, which provides the direct search algorithm proposed by Nelder and Mead for model optimization.
Maintained by Sebastien Bihorel. Last updated 3 years ago.
2.70 score
mdubel
bdsm:Bayesian Dynamic Systems Modeling
Implements methods for building and analyzing models based on panel data as described in the paper by Moral-Benito (2013, <doi:10.1080/07350015.2013.818003>). The package provides functions to estimate dynamic panel data models and analyze the results of the estimation.
Maintained by Marcin Dubel. Last updated 16 days ago.
2.00 score
olobatuyi
extBatchMarking:Extended Batch Marking Models
A system for batch-marking data analysis to estimate survival probabilities, capture probabilities, and enumerate the population abundance for both marked and unmarked individuals. The estimation of only marked individuals can be achieved through the batchMarkOptim() function. Similarly, the combined marked and unmarked can be achieved through the batchMarkUnmarkOptim() function. The algorithm was also implemented for the hidden Markov model encapsulated in batchMarkUnmarkOptim() to estimate the abundance of both marked and unmarked individuals in the population. The package is based on the paper: "Hidden Markov Models for Extended Batch Data" of Cowen et al. (2017) <doi:10.1111/biom.12701>.
Maintained by Kehinde Olobatuyi. Last updated 6 months ago.
1.70 score 8 scripts