TesiproV 1.0.1

Tests only: fixes the test failures on macOS reported in the CRAN checks of version 1.0.0. The package code is unchanged.

TesiproV 1.0.0

Consolidated release of the package, based on more than two years of use. New in this version: the influence of quality controls on the distribution of basic variables can be taken into account.

New: quality control

• The influence of quality controls on the distribution of a basic variable can now be taken into account. A sampling plan is defined with the new class PROB_QUALCON. The conformity criterion of EN 206 for the compressive strength ("fck_EN-206") and an attribute plan for dimensions and other characteristics ("dim_k||n") are available. The acceptance probability of a plan (OC curve) can be computed, plotted or entered manually.
• PROB_BASEVAR$updateBasevar() returns the basic variable after acceptance of the lot (posterior distribution). The prior must be a Student-t ("st") or log-Student-t ("lt") distribution with hyperparameters. The message compares mean value and standard deviation of prior and posterior.
• The vignette contains a new section with examples.

System reliability

• The importance sampling for systems ("MCIS") was revised. It estimates the system failure probability more accurately and also handles parallel systems whose design points are far apart.
• Available methods of calculateSystemProbability(): "simpleBounds", "MCIS", "MCC" (crude Monte Carlo) and "MCSUS" (subset simulation). FORM and SORM provide single results, which enter the simple bounds. A system analysis by FORM or SORM is not available.
• Variables that are shared by several limit-state functions must have the same Name.
• The effective sample size reported by MC_IS now counts the samples in the failure domain.

Other changes

• FORM with optim_type = "auto" (default) switches to a second solver if the first one does not converge. The result field fallback shows whether this happened.
• The console output of the Monte Carlo methods is reduced and controlled by debug.level.
• PROB_BASEVAR$plot() plots the probability density of a basic variable. For a variable created by updateBasevar(), the prior distribution can be added with prior = TRUE and the fitted curve and the empirical resample with compare = TRUE.
• Option descriptions and notes on parallel computing in the vignette were updated.

TesiproV 0.9.6

MC_IS estimator correction and reliability analysis improvements

Monte Carlo Importance Sampling (MC_IS)

• Corrected the MC_IS estimator to remove bias caused by the previous self-normalized implementation.
• Refactored worker aggregation in MC_IS_single to ensure correct accumulation of weighted samples and consistent global statistics.
• Workers now compute weights directly and return aggregated statistics instead of raw sample vectors.

System reliability analysis

• Fixed incorrect propagation of sys_type from calculateSystemProbability() to MC_IS_system(), ensuring that serial and parallel systems are evaluated correctly.
• Resolved cases of uninitialized variables in the system worker (mode_ids, shift, and references to I_matrix).
• Enabled multimodal sampling for both serial and parallel systems.

Performance improvements

• Optimized the transformation from u-space to x-space by removing large intermediate matrices and computing quantiles column-wise.
• Replaced large failure indicator matrices with incremental counters to significantly reduce memory usage.
• Reduced worker-to-master communication in parallel runs by returning only aggregated statistics.

Validation and testing

• Added regression tests for MC_IS_single, including linear limit-state cases, non-normal distributions, and rare-event scenarios.
• Added system reliability tests comparing results with MC_CRUDE.
• Verified consistency with FORM results within expected Monte Carlo error.

TesiproV 0.9.5

Major refactoring of object classes

System and reliability objects

• Reworked all reference classes (SYS_PROB, SYS_PARAM, SYS_LSF, PROB_BASEVAR, …).
• Introduced explicit constructors with argument validation for better error handling.
• Improved consistency between mean, standard deviation and coefficient of variation.
• Added robust forward/backward transformations for all supported distributions.
• Fixed handling of zero mean values (Cov <- Inf) to reflect infinite relative scatter.

Limit-state functions

• Field type for func changed from "function" to "ANY" with explicit default NULL. This prevents creation of dummy closures and allows proper missing-function detection.
• Enhanced $check() method:
  • Distinguishes between missing, empty, and valid limit-state functions.
  • Provides informative error messages used by automated tests.

Parametric study objects

• Constructor of SYS_PARAM now correctly forwards arguments to parent class (callSuper(...)) ensuring that inherited fields like sys_input are preserved.
• Parallel execution restructured using the global future plan set in .onLoad(). The outer level is capped by environment variable TesiproV.max_workers (default: 4 cores) while inner Monte-Carlo methods manage their own threads.

Probabilistic base variables

• Complete rewrite of transformation logic in $prepare():
  • Supports normal, lognormal, Gumbel, gamma, beta and Weibull distributions consistently.
  • Corrected implementation of the Gumbel distribution
    (location parameter now properly computed as
    location = Mean + digamma(1) * scale; scale derived via (Sd * sqrt(6)) / π).
  • Added numerical root search for Weibull shape parameter when empirical formula fails.
  • Added empirical, shifted lognormal, Student-t and logStudent-t distribution.
• Distribution caching via digest hash implemented in $getlDistr() for performance improvement.

Package infrastructure

• Added internal .onLoad() function for package-specific default options that can later be used by TesiproV functions to configure parallel execution in a controlled and CRAN-compliant way.

Minor improvements

• Better formatted output in $printResults().
• Consistent English comments throughout codebase for clarity and documentation generation via roxygen2.
• Updated examples in help pages to match new constructors and usage patterns.

Compatibility notes

Existing scripts using older class definitions should continue to work after minor adjustments: replace direct field assignments by constructor arguments where applicable, and ensure that limit-state functions are defined before calling $check().

TesiproV 0.9.2.0

Better implementation of parallel computing in Unix and macOS environments.
Changed RNG handling in parallel approach.

TesiproV 0.9.1.0

• First release.

© 2021–2026 T. Lux, K. Nille-Hauf, T. Feiri, M. Ricker – Hochschule Biberach (until 2022), TU Dortmund University – Chair of Structural Concrete (since 2023)