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>.

Last updated 4 months ago

financehidden-markov-modelscppopenmp

7.06 score 16 stars 5 scripts 444 downloads

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>.

Last updated 3 months ago

bayesdiscrete-choiceprobitopenblascppopenmp

5.62 score 2 stars 1 scripts 319 downloads

oeli - Utilities for Developing Data Science Software

Some general helper functions that I (and maybe others) find useful when developing data science software.

Last updated 2 months ago

openblascpp

5.51 score 2 stars 4 dependents 1 scripts 797 downloads

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.

Last updated 6 months ago

optimization

4.90 score 2 stars 2 scripts 545 downloads

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.

Last updated 2 months ago

optimization

4.82 score 4 stars 1 dependents 7 scripts 648 downloads

vntrs - Variable Neighborhood Trust Region Search

An implementation of the variable neighborhood trust region algorithm Bierlaire et al. (2009) "A Heuristic for Nonlinear Global Optimization" <doi:10.1287/ijoc.1090.0343>.

Last updated 1 years ago

optimization

2.70 score 1 scripts 127 downloads

portion - Extracting a Data Portion

Provides a simple method to extract portions of a vector, matrix, or data.frame. The relative portion size and the way the portion is selected can be chosen.

Last updated 1 years ago

2.70 score 2 scripts 112 downloads

normalize - Centering and Scaling of Numeric Data

Provides a simple method for centering and scaling of numeric data. Certain columns or rows can be ignored when normalizing or be normalized jointly.

Last updated 1 years ago

2.70 score 5 scripts 588 downloads