Showing 4 of total 4 results (show query)
loelschlaeger
fHMM:Fitting Hidden Markov Models to Financial Data
Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) <doi:10.1177/1471082X211034048> for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) <doi:10.18637/jss.v109.i09>.
Maintained by Lennart Oelschläger. Last updated 7 days ago.
financehidden-markov-modelscppopenmp
17 stars 7.04 score 5 scriptsloelschlaeger
RprobitB:Bayesian Probit Choice Modeling
Bayes estimation of probit choice models, both in the cross-sectional and panel setting. The package can analyze binary, multivariate, ordered, and ranked choices, as well as heterogeneity of choice behavior among deciders. The main functionality includes model fitting via Markov chain Monte Carlo m ethods, tools for convergence diagnostic, choice data simulation, in-sample and out-of-sample choice prediction, and model selection using information criteria and Bayes factors. The latent class model extension facilitates preference-based decider classification, where the number of latent classes can be inferred via the Dirichlet process or a weight-based updating heuristic. This allows for flexible modeling of choice behavior without the need to impose structural constraints. For a reference on the method see Oelschlaeger and Bauer (2021) <https://trid.trb.org/view/1759753>.
Maintained by Lennart Oelschläger. Last updated 6 months ago.
bayesdiscrete-choiceprobitopenblascppopenmp
4 stars 5.45 score 1 scriptsloelschlaeger
ao:Alternating Optimization
Alternating optimization is an iterative procedure that optimizes a function by alternately performing restricted optimization over individual parameter subsets. Instead of tackling joint optimization directly, it breaks the problem down into simpler sub-problems. This approach can make optimization feasible when joint optimization is too difficult.
Maintained by Lennart Oelschläger. Last updated 8 months ago.
2 stars 4.78 score 2 scriptsloelschlaeger
optimizeR:Unified Framework for Numerical Optimizers
Provides a unified object-oriented framework for numerical optimizers in R. Allows for both minimization and maximization with any optimizer, optimization over more than one function argument, measuring of computation time, setting a time limit for long optimization tasks.
Maintained by Lennart Oelschläger. Last updated 2 days ago.
4 stars 4.62 score 7 scripts 1 dependents