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>.

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financehidden-markov-modelscppopenmp

6.28 score 20 stars 12 scripts 558 downloads

RprobitB - Bayesian Probit Choice Modeling

Bayes estimation of probit choice models in cross-sectional and panel settings. 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 Gibbs sampling, 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>.

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bayesdiscrete-choiceprobitopenblascppopenmp

5.24 score 5 stars 3 scripts 300 downloads

oeli - Some Utilities for Developing Data Science Software

A collection of general-purpose helper functions that I (and maybe others) find useful when developing data science software. Includes tools for simulation, data transformation, input validation, and more.

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openblascppopenmp

5.23 score 2 stars 8 dependents 2 scripts 869 downloads

ao - Alternating Optimization

Implementation of an iterative process that optimizes a function by alternately performing restricted optimization over parameter subsets. Instead of solving one joint optimization problem, alternating optimization breaks it into smaller sub-problems. This approach can make optimization feasible when joint optimization is too difficult.

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optimization

4.88 score 3 stars 4 scripts 345 downloads

optimizeR - Unified Framework for Numerical Optimizers

Provides a unified object-oriented framework for numerical optimizers in R. Supports minimization and maximization with any optimizer, optimization over more than one function argument, computation time measurement, and time limits for long optimization tasks.

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optimization

4.68 score 4 stars 4 dependents 8 scripts 332 downloads

ino - Initialization of Numerical Optimization

Analysis of the initialization for numerical optimization of real-valued functions, particularly likelihood functions of statistical models. See <https://loelschlaeger.de/ino/> for more details.

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optimization

4.48 score 2 stars 232 downloads

trackopt - Track Numerical Optimization

Tracks parameter values, gradients, and Hessians at each iteration of numerical optimizers. Useful for analyzing optimization progress, diagnosing issues, and studying convergence behavior.

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optimization

3.18 score 1 scripts 180 downloads

choicedata - Working with Choice Data

Offers a set of objects tailored to simplify working with choice data. It enables the computation of choice probabilities and the likelihood of various types of choice models based on given data.

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3.18 score 1 stars 1 scripts 190 downloads

portion - Extracting a Data Portion

Provides simple methods to extract data portions from various objects. The relative portion size and the way the portion is selected can be chosen.

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3.18 score 1 dependents 2 scripts 155 downloads

normalize - Centering and Scaling of Numeric Data

Provides simple methods for centering and scaling of numeric data. Columns or rows can be ignored when normalizing or be normalized jointly.

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3.18 score 1 dependents 7 scripts 314 downloads

vntrs - Variable Neighborhood Trust Region Search

Implements the variable neighborhood trust region search (VNTRS) algorithm for nonlinear global optimization, following Bierlaire et al. (2009) "A Heuristic for Nonlinear Global Optimization" <doi:10.1287/ijoc.1090.0343>. The method combines neighborhood exploration with a trust-region framework to search the solution space efficiently. It can terminate a local search early when the iterates converge toward a previously visited local optimum or when further improvement within the current region is unlikely. The algorithm can also be used to identify multiple local optima.

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openblascppopenmp

3.00 score 2 scripts 226 downloads