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  "Title": "Sampling Methods and Distribution Functions for the Ising Model",
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  "Description": "Sample states from the Ising model and compute the\nprobability of states. Sampling can be done for any number of\nnodes, but due to the intractability of the Ising model the\ndistribution can only be computed up to roughly 10 nodes. The\nBlume-Capel model, an Ising model with an additional on-site\nquadratic (crystal-field) term, is also supported.",
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  "URL": "https://github.com/SachaEpskamp/IsingSampler",
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  "Date/Publication": "2026-05-30 14:49:52 UTC",
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    "author": "Sacha Epskamp <sacha.epskamp@gmail.com>",
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    "message": "Add BlumeCapelSampler and a delta (quadratic/crystal-field) term; bump to 0.4.0\n\nAdds the Blume-Capel model: an Ising model with an on-site quadratic\n(\"single-ion anisotropy\" / crystal-field) term, so\n\n  H = - sum_i tau_i s_i - sum_{i<j} omega_ij s_i s_j + sum_i delta_i s_i^2.\n\n- New BlumeCapelSampler(): thin wrapper around IsingSampler() with default\n  responses c(-1, 0, 1) and a required 'delta'; method \"MH\" or \"direct\".\n- 'delta' argument added to IsingSampler(), IsingDir(), IsingLikelihood(),\n  IsingSumLikelihood() and IsingStateProb() (default 0 = ordinary Ising).\n  A single value (or a per-node vector) is accepted; a single 'thresholds'\n  value is now likewise recycled over nodes.\n- C++: H() and f() take an optional delta (NULL = pure Ising); the Pcat\n  softmax weight becomes beta*(r_k*F - delta_i*r_k^2); IsingMet/IsingProcess/\n  IsingSamplerCpp thread delta through. CFTP stays binary-only and folds\n  delta exactly into the thresholds (delta*s^2 is affine for two responses).\n- Regenerated RcppExports.*; updated docs/NEWS/DESCRIPTION. Passes\n  R CMD check (0 errors / 0 warnings / 0 notes).\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n",
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  "_updates": [
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  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/IsingSampler.html",
    "extra/NEWS.html",
    "extra/NEWS.txt",
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    "extra/readme.md",
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      "date": "2014-01-09"
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      "date": "2015-03-02"
    },
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      "date": "2020-01-25"
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    "BlumeCapelSampler",
    "EstimateIsing",
    "EstimateIsingBi",
    "EstimateIsingLL",
    "EstimateIsingPL",
    "EstimateIsingUni",
    "IsingEntrophy",
    "IsingLikelihood",
    "IsingPL",
    "IsingSampler",
    "IsingStateProb",
    "IsingSumLikelihood",
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      "page": "IsingSampler-package",
      "title": "Sampling methods and distribution functions for the Ising model",
      "topics": [
        "IsingSampler-package"
      ]
    },
    {
      "page": "BlumeCapelSampler",
      "title": "Sample states from the Blume-Capel model",
      "topics": [
        "BlumeCapelSampler"
      ]
    },
    {
      "page": "EstimateIsing",
      "title": "non-regularized estimation methods for the Ising Model",
      "topics": [
        "EstimateIsing",
        "EstimateIsingBi",
        "EstimateIsingLL",
        "EstimateIsingPL",
        "EstimateIsingUni"
      ]
    },
    {
      "page": "IsingEntrophy",
      "title": "Entropy of the Ising Model",
      "topics": [
        "IsingEntrophy"
      ]
    },
    {
      "page": "IsingLikelihood",
      "title": "Likelihood of all states from tractible Ising model.",
      "topics": [
        "IsingLikelihood"
      ]
    },
    {
      "page": "IsingPL",
      "title": "Pseudo-likelihood",
      "topics": [
        "IsingPL"
      ]
    },
    {
      "page": "IsingSampler",
      "title": "Sample states from the Ising model",
      "topics": [
        "IsingSampler"
      ]
    },
    {
      "page": "IsingStateProb",
      "title": "Likelihood of single state from tractible Ising model.",
      "topics": [
        "IsingStateProb"
      ]
    },
    {
      "page": "IsingSumLikelihood",
      "title": "Likelihood of sumscores from tractible Ising model.",
      "topics": [
        "IsingSumLikelihood"
      ]
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      "title": "Transform parameters for linear transformations on response catagories",
      "topics": [
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      ]
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