Package | Description |
---|---|
com.mockturtlesolutions.snifflib.integration |
Contains standard classes and interfaces for performing integration.
|
com.mockturtlesolutions.snifflib.invprobs |
Contains standard classes and interfaces for solving statistical inverse problems.
|
com.mockturtlesolutions.snifflib.statmodeltools.database |
Base classes for the repository storage framework of the statmodeltools package.
|
com.mockturtlesolutions.snifflib.statmodeltools.workbench |
Graphical interface classes for the repository storage framework of the statmodeltools package.
|
com.mockturtlesolutions.snifflib.stats |
Contains standard classes and interfaces for statistical storage classes and algorithms.
|
com.mockturtlesolutions.snifflib.testfun |
Contains some typical functions useful for testing various algorithms.
|
com.mockturtlesolutions.snifflib.util |
Contains some utility classes providing methods of broad applicability.
|
com.mockturtlesolutions.snifflib.xppauttools.database |
Base classes for the repository storage framework of xppauttools
|
Modifier and Type | Class and Description |
---|---|
class |
OdeModel
This model wraps an OdeSolver and extends the StatisticalModel.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractProposalDistribution
A standard approach to the proposal distribution called for in Metropolis-Hastings Markov Chain
Monte-Carlo.
|
class |
BayesianLinear
This is a simple extension of Linear appropriate for full Bayesian linear regression
under the assumption of independent and identically distributed Gaussian errors and
uniform prior.
|
class |
DistributedStatisticalModel
Provides a transparent extension of StatisticalModel for cases where parameters and/or data may be distributed
across one or more other classes (e.g.
|
class |
GaussianProposal
A convenience class for setting up a proposal distribution which
is Gaussian for all parameters in a companion StatisticalModel.
|
class |
StatisticalModel
This is a class which implements NamedParameters can make predictions as a function of those parameters
and can have its parameters optimized.
|
Modifier and Type | Interface and Description |
---|---|
interface |
GLMStorage
One of the core statistical model storages.
|
interface |
StatisticalModelStorage |
Modifier and Type | Class and Description |
---|---|
class |
GLMStorageDOM |
class |
GLMStoragePostgreSQL |
class |
GLMStorageSQL |
class |
GLMStorageXML |
class |
StatisticalModelPostgreSQL |
class |
StatisticalModelSQL |
class |
StatisticalModelXML |
Modifier and Type | Class and Description |
---|---|
class |
GLMStorageFrame |
Modifier and Type | Class and Description |
---|---|
class |
FCDF |
class |
NormPDF |
Modifier and Type | Class and Description |
---|---|
class |
Constant |
class |
Cubic |
class |
HollingI |
class |
HollingII |
class |
HollingIII |
class |
Linear |
class |
MichaelisMenten |
class |
Quadratic |
Modifier and Type | Class and Description |
---|---|
class |
AbstractGammaAlgorithm
Gamma function evaluation.
|
class |
AbstractIncBetaAlgorithm
Incomplete Beta function.
|
class |
AbstractLogGammaAlgorithm
Gamma function evaluation.
|
class |
NRIncBetaAlgorithm
Algorithm is based on Numerical Recipies in Fortran p.220-221.
|
class |
NRLogGammaAlgorithm
Algorithm is based on Numerical Recipies in Fortran.
|
class |
TothGammaAlgorithm
Algorithm is based on an approximation presented by Viktor T.
|
Modifier and Type | Class and Description |
---|---|
class |
XppOdeStatisticalModel |
Copyright © 2011, 2013. Daniel P. Dougherty