[{"_1":2,"_160":-5,"_161":-5},"loaderData",{"_3":4,"_17":18,"_25":26,"_139":140},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7,"_13":14,"_15":16},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","theme","system","contrast","normal","routes/_layout",{"_19":-5,"_20":21,"_22":21,"_23":11,"_24":21},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_27":28,"_29":-5,"_30":11,"_31":11,"_32":33,"_34":35,"_36":-5,"_37":11,"_38":39,"_88":-5,"_89":90,"_128":-5,"_129":11,"_130":21,"_131":11,"_132":-5,"_133":11,"_134":-5,"_135":136,"_137":91,"_138":-7},"canonicalUrl","https://hub.docker.com/r/terhorst/smcpp/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","terhorst","reponame","smcpp","cloudBuildSource","isAutobuildsAvailable","repo",{"_40":41,"_42":43,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":55,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":62,"_64":65,"_66":-5,"_67":68,"_69":70,"_71":11,"_72":33,"_73":74,"_79":11,"_80":81,"_85":33,"_86":87},"categories",[],"content_types",[44],"image","date_registered","2020-06-18T17:58:43.710445Z","description","","is_private","last_updated","2021-03-16T19:38:15.683075Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[56],"application/vnd.docker.container.image.v1+json","name","pull_count",28064,"repository_type","star_count",1,"status","status_description","active","source","collaborator_count",0,"full_description",".. image:: https://travis-ci.org/popgenmethods/smcpp.svg?branch=master \n :target: https://travis-ci.org/popgenmethods/smcpp\n \nSMC++ is a program for estimating the size history of populations from\nwhole genome sequence data.\n\n.. contents:: :depth: 2\n\nQuick start guide\n=================\n\n1. Follow the `installation instructions`_.\n \n2. Convert your VCF(s) to the SMC++ input format with vcf2smc_::\n\n $ smc++ vcf2smc my.data.vcf.gz out/chr1.smc.gz chr1 Pop1:S1,S2\n\n This command will parse data for the contig ``chr1`` for samples\n ``S1`` and ``S2`` which are members of population ``Pop1``. You\n should run this once for each independent contig in your dataset,\n producing one SMC++ output file per contig.\n\n3. Fit the model using estimate_::\n\n $ smc++ estimate -o analysis/ 1.25e-8 out/example.chr*.smc.gz\n\n The first mandatory argument, ``1.25e-8``, is the per-generation\n mutation rate. The remaining arguments are the data files generated\n in the previous step. Depending on sample size and your machine,\n the fitting procedure should take between a few minutes and a\n few hours. The fitted model will be stored in JSON format in\n ``analysis/model.final.json``.\n\n4. Visualize the results using plot_::\n\n $ smc++ plot plot.pdf analysis/model.final.json\n\nSMC++ can also estimate and plot joint demographies from pairs of\npopulations; see split_.\n\n.. _latest release: https://github.com/popgenmethods/smcpp/releases/latest\n\nInstallation instructions\n=========================\n\nBeginning with v1.15.4, SMC++ is distributed as a Docker image. (Anaconda\nsupport has been discontinued.) To run the latest version of the program,\ntype::\n\n $ docker run --rm -v $PWD:/mnt terhorst/smcpp:latest [ARGUMENTS]\n\nTo run a specific version of the program, change `latest` to `version-` followed\nby the version number. For example, to run v1.15.4, type::\n\n $ docker run --rm -v $PWD:/mnt terhorst/smcpp:version-1.15.4 [ARGUMENTS]\n \nIf ``docker`` is unavailable (for example, in a cluster environment where you do not\nhave admin privileges), some users have reported success using singularity_ instead.\n\n.. _singularity: https://sylabs.io/guides/3.7/user-guide/\n\nBuild instructions\n==================\nSMC++ requires the following libraries and executables in order compile and run:\n\n- Python 3.7 or greater.\n- A C++-11 compiler (gcc 4.8 or later, for example).\n- gmp_, for some rational field computations.\n- mpfr_ (at least version 3.0.0), for some extended precision calculations.\n- gsl_, the GNU Scientific Library.\n\nOn Ubuntu (or Debian) Linux, the library requirements may be installed\nusing the commmand::\n\n $ sudo apt-get install -y python3-dev libgmp-dev libmpfr-dev libgsl0-dev\n\nOn OS X, the easiest way to install them is using Homebrew_::\n\n $ brew install mpfr gmp gsl\n\nAfter installing the requirements, SMC++ may be built by running::\n \n $ pip install git+https://github.com/popgenmethods/smcpp\n\n(Alternatively, ``git clone`` the repository and run the usual \n``python setup.py install``. You *must* clone. Downloading the source\ntarball will not work.)\n\n.. _Homebrew: http://brew.sh\n.. _gmp: http://gmplib.org\n.. _mpfr: http://mpfr.org\n.. _gsl: https//www.gnu.org/software/gsl/\n\nNote for OS X users\n-------------------\nVersions of Clang shipping with Mac OS X do not currently support\nOpenMP_. In order to build SMC++ on OS X you must use a compiler that\ndoes, such as ``gcc``::\n\n $ brew install gcc\n $ CC=gcc-6 CXX=g++-6 python setup.py install\n\n.. _OpenMP: http://openmp.org\n\nVirtual environment\n-------------------\nSMC++ pulls in a fair number of Python dependencies. If you prefer to\nkeep this separate from your main Python installation, or do not have\nroot access on your system, you may wish to install SMC++ inside of a\n`virtual environment`_. To do so, first create and activate the virtual\nenvironment::\n\n $ virtualenv -p python3 \n $ source /bin/activate\n\nThen, install SMC++ as described above.\n\n.. _virtual environment: http://docs.python-guide.org/en/latest/dev/virtualenvs/\n\nUsage\n=====\n\nSMC++ comprises several subcommands which are accessed using the\nsyntax::\n\n $ smc++ \n\nwhere ```` is one of the following:\n\nvcf2smc\n-------\n\nThis subcommand converts (biallelic, diploid) VCF data to the format\nused by SMC++. \n\nRequired arguments\n^^^^^^^^^^^^^^^^^^\n\n1. An `indexed VCF file `_.\n2. An output file. Appending the ``.gz`` extension will cause the output\n to be compressed; the estimate_ command can read from both compressed\n and uncompressed data sources.\n3. A contig name. Each call to vcf2smc_ processes a single contig. \n VCFs containing multiple contigs should be processed via multiple\n separate runs.\n4. A list of population(s) and samples. Each population has an id followed\n by a comma-separated list of sample IDs (column names in the VCF). Up to\n two populations are supported.\n\nFor example, to convert contig ``chr1`` of ``vcf.gz`` using samples\n``NA12878`` and ``NA12879`` of population ``CEU``, saving to\n``chr1.smc.gz``, use::\n\n $ smc++ vcf2smc vcf.gz chr1.smc.gz chr1 CEU:NA12878,NA12879\n\nOptional arguments\n^^^^^^^^^^^^^^^^^^\n- ``-d``: SMC++ relies crucially on the notion of a pair of *distinguished lineages*\n (see paper for details on this terminology). The identity of the\n distinguished lineages is set using the ``-d`` option, which specifies\n the sample(s) which will form the distinguished pair. ``-d`` accepts to\n sample ids. The first allele will be taken from sample 1 and the second\n from sample 2. To form the distinguished pair using one\n haplotype from each of ``NA1287{8,9}`` using the above example::\n \n $ smc++ vcf2smc -d NA12878 NA12879 vcf.gz chr1.smc.gz chr1 CEU:NA12878,NA12879\n \n Note that \"first\" and \"second\" allele have no meaning for unphased data; if your\n data are not phased, it only makes sense to specify a single individual \n (e.g. ``-d NA12878 NA12878``).\n\n .. _masking:\n\n- ``--mask``, ``-m``: This specifies a BED-formatted mask file whose\n positions will be marked as missing data (across all samples) in\n the outputted SMC++ data set. This can be used to delineate large\n uncalled regions (e.g. centromeres) which are often omitted in VCF\n files; without additional information provided by ``--mask``, there\n is no way to distinguish these missing regions from very long runs\n of homozygosity. For finer-grained control of missing data, setting\n individual positions and samples to the missing genotype, ``./.``,\n also works fine. (The point of ``--mask`` is to save the user the\n trouble of emitting millions of rows of missing observations in the\n VCF).\n\n- ``--missing-cutoff``, ``-c``: This is an alternative to ``--mask`` which will\n automatically treat runs of homozgosity longer than ``-c`` base pairs\n as missing. Typically ``-c`` should be set high so as not\n to filter out legitimate long runs of homozyous bases, which are\n informative about recent demography. This is a fairly crude approach\n to filtering and is only recommended for use in cases where using\n ``--mask`` is not possible.\n \nComposite likelihood\n^^^^^^^^^^^^^^^^^^^^\nBy varying ``-d`` over the same VCF, you can create distinct data\nsets for estimation. This is useful for forming composite likelihoods.\nFor example, the following command will create three data sets from\ncontig ``chr1`` of ``myvcf.gz``, by varying the identity of the distinguished\nindividual and treating the remaining two samples as \"undistinguished\":\n\n.. code-block:: bash\n\n for i in {7..9}; \n do smc++ vcf2smc -d NA1287$i NA1287$i myvcf.gz out.$i.txt chr1 NA12877 NA12878 NA12890; \n done\n\nYou can then pass these data sets into estimate_::\n\n $ smc++ estimate -o output/ out.*.txt\n\nSMC++ treats each file ``out.*.txt`` as an independently evolving\nsequence (i.e., a chromosome); the likelihood is simply the product\nof SMC++ likelihoods over each of the data sets. In the example above\nwhere the data sets are generated from the same chromosome but different\ndistinguished individuals (different ``-d``), this independence\nassumption is violated, leading to a so-called **composite likelihood**.\nThe advantage of this approach is that it incorporates genealogical\ninformation from additional distinguished individuals into the analysis,\npotentially leading to improved estimates. \n\nSince (a portion of) the computational and memory requirements of SMC++\nscale linearly with the total analyzed sequence length, it is generally\nadvisable to composite over a relatively small number of individuals. In\npractice we generally use 2-10 individuals, depending on genome length,\nsample size, etc., and have found that this leads to improved estimation\nwithout causing significant degeneracy in the likelihood.\n\nCaveats\n^^^^^^^\n``vcf2smc`` targets a common use-case but may not be sufficient for all\nusers. In particular, you should be aware that:\n\n- The ancestral allele is assumed to be the reference allele.\n- The FILTER and QUAL columns are ignored.\n- Indels, structural variants, and any non-SNP data are ignored.\n- For sites containing multiple entries in the VCF, all but the first\n entry is ignored.\n- Sites which are not present in the VCF are assumed to be homoyzgous\n ancestral across all samples. (See masking_, above.)\n\nThose wishing to implement their own custom conversion to the SMC++\ndata format should see the `input data format`_ description below.\n\nestimate\n--------\n\nThis command will fit a population size history to data. The basic usage\nis::\n\n $ smc++ estimate [ ...]\n\n\nRequired arguments\n^^^^^^^^^^^^^^^^^^\n\n1. The per-generation mutation rate. Scientific notation is acceptable: use\n e.g. ``1e-8`` in place of ``.00000001``.\n2. One or more SMC++-formatted data files, generated by vcf2smc_, for example.\n\nOptional arguments\n^^^^^^^^^^^^^^^^^^\n- ``-o``: specifies the directory to store the final estimates as well as\n all intermediate files and debugging output. Defaults to ``.``, i.e. the\n current working directory.\n- ``--polarization-error``: if the identity of the ancestral\n allele is not known, these options can be used to specify a prior over it.\n With polarization error ``p``, emissions probabilities for entry ``CSFS(a,b)``\n will be computed as ``(1-p) CSFS(a,b) + p CSFS(2-a, n-b)``. The default setting\n is ``0.5``, i.e. the identity of the ancestral allele is not known.\n- ``--unfold`` is an alias for ``--polarization-error 0``. If the\n ancestral allele is known (from an outgroup, say) then this option will\n use the unfolded SFS for computing probabilities. Incorrect usage of\n this feature may lead to erroneous results.\n\nA number of other arguments concerning technical aspects of the fitting\nprocedure exist. To see them, pass the ``-h`` option to ``estimate``.\n\ncv\n--\n\nThis command is similar to estimate_, with the difference that it uses\ncross-validation to obtain sensible model parameters for use during estimation.\nThe syntax and options for this command are nearly identical to estimate_:\n\n $ smc++ cv [ ...]\n\nThe optional `--folds` parameter can be used to specify the number of folds\nused for performing `k`-fold cross validation. The default is `2` and should be\nset higher in cases where you have more data.\n\n**Important**: The `cv` command was not part of the original paper; in\nparticular, it has not been peer-reviewed. Although it has proved useful in some\ncontexts, manual parameter tuning may still be necessary.\n\nplot\n----\n\nThis command plots fitted size histories. The basic usage is::\n\n $ smc++ plot plot.png model1.json model2.json [...] modeln.json\n\nwhere ``model*.json`` are fitted models produced by ``estimate``.\n\nRequired arguments\n^^^^^^^^^^^^^^^^^^\n\n1. An output file-name. The output format is determined by the extension\n (``.pdf``, ``.png``, ``.jpeg``, etc.)\n2. One or more JSON-formatted SMC++ models (the output from estimate_).\n\nOptional arguments\n^^^^^^^^^^^^^^^^^^\n\n- ``-g`` sets the generation time (in years) used to scale the x-axis. If not\n given, the plot will be in coalescent units.\n- ``--logy`` plots the y-axis on a log scale.\n- ``-c`` produces a CSV-formatted table containing the data used to generate\n the plot.\n\nsplit\n-----\n\nThis command fits two-population clean split models using marginal\nestimates produced by estimate_. To use ``split``, first estimate each\npopulation marginally using ``estimate``::\n\n $ smc++ vcf2smc my.vcf.gz data/pop1.smc.gz pop1:ind1_1,ind1_2\n $ smc++ vcf2smc my.vcf.gz data/pop2.smc.gz pop2:ind2_1,ind2_2\n $ smc++ estimate -o pop1/ data/pop1.smc.gz\n $ smc++ estimate -o pop2/ data/pop2.smc.gz\n\nNext, create datasets containing the joint frequency spectrum for both\npopulations::\n\n $ smc++ vcf2smc my.vcf.gz data/pop12.smc.gz pop1:ind1_1,ind1_2 pop2:ind2_1,ind2_2\n $ smc++ vcf2smc my.vcf.gz data/pop21.smc.gz pop2:ind2_1,ind2_2 pop1:ind1_1,ind1_2\n\nFinally, run ``split`` to refine the marginal estimates into an estimate\nof the joint demography::\n\n $ smc++ split -o split/ pop1/model.final.json pop2/model.final.json data/*.smc.gz\n $ smc++ plot joint.pdf split/model.final.json\n\nposterior\n---------\nThis command will export (and optionally visualize) the posterior\ndistribution of the time to most recent common ancestor (TMRCA) in the\ndistinguished pair from the given data set.\n\nThe output file is the result of::\n\n >>> numpy.savez(output, hidden_states=hs, \n **{'file1'=gamma1, 'file1_sites'=sites1, ...})\n\nwhere:\n\n- ``hs`` is a vector of length ``M + 1`` indicating the breakpoints used\n to discretize the hidden TMRCA of the distinguished pair. The\n breakpoints are chosen such that the probability of coalescence \n within each interval is uniform with respect to the fitted model.\n- ``sites1`` is the vector of length ``L`` containing positions where the\n decoding is performed for data set ``file1``. Due to the internal archtecture of SMC++,\n there is one entry per row in the data set.\n- ``gamma1`` is an array of dimension ``M x L`` whose entry \n ``gamma1[m, ell]`` gives the average posterior probability of coalescence in interval\n ``[hs[m], hs[m + 1])`` for each site in the interval \n ``{sites1[ell], ..., sites1[ell + 1] - 1}``.\n \nThere will be a ``gamma``/``sites`` entry for each data set decoded.\n\nRequired arguments\n^^^^^^^^^^^^^^^^^^\n- ``model``: A fitted SMC++ model, i.e. the ``model.final.json`` outputted\n by estimate_.\n- ``output``: A file name to save the posterior decoding arrays, in the format\n shown above.\n- ``data``: One or more data sets in SMC++ format, i.e. the output of vcf2smc_. \n\nOptional arguments\n^^^^^^^^^^^^^^^^^^\n- ``--heatmap plot.(png|pdf|jpg)``: Also produce a heatmap of the posterior \n decoding. The output format is given by the extension.\n- ``--start s``, ``--end e``: For regions that are much longer than ~1cM, \n the heatmap will look pretty noisy. These options can be used to narrow\n in on specific regions of the chromosome.\n- ``--colorbar``: Also add a colorbar showing the scale of the heatmap.\n\n\ncite\n----\n\nThis command prints plain- and BibTex-formatted citation information for\nthe `accompanying paper`_ to the console.\n\n.. _accompanying paper: http://www.nature.com/ng/journal/vaop/ncurrent/ng.3748\n\n\nTips for using SMC++\n====================\n\nSMC++ has several regularization parameters which affect the quality of\nthe fits obtained using estimate_ and split_. The default settings have\nproved useful for analyzing high coverage human sequence data from a few\nhundred individuals. For other types of data, *you will likely need to\nexperiment with different values of these parameters in order to obtain\ngood estimates*.\n\n- ``--thinning``: This parameter controls the frequency with which the full\n CSFS is emitted (see paper for details). Decreasing the value of this parameter will cause the likelihood\n to depend more strongly on frequency spectrum information in the undistinguished\n portion of the sample, potentially leading to more accurate results in the recent\n past. However, decreasing it too much can lead to degeneracy in the likelihood since\n correlations in the undistinguished portion of the ancestral recombination graph are\n ignored. The default value for a sample size ``n`` is ``1000 * log(n)`` \n (note that this is different than in versions 1.7.0 and earlier). Empirically,\n this has worked well for sample sizes on the order of ``20 <= n <= 200`` but you\n may need to experiment a bit.\n \n- ``--spline``: Controls the functional form used to fit the model. \n The default value in recent versions is ``piecewise`` to better match the output from\n `{P,M}SMC`. To enable cubic splines (what is used in the paper), use ``--spline cubic`` or ``--spline pchip``.\n (For details on the differences between cubic and pchip splines see `here `_.)\n\n- ``--timepoints``: This command specifies the starting and ending time points\n of the model. It accepts two numbers `t1 tK` specifying the starting and\n ending time points of the model (in generations). If not specified, SMC++\n will use an heuristic to calculate the model time points points\n automatically.\n\n- ``--regularization-penalty``, ``-rp``: This parameter penalizes curvature in\n the estimated size history. The default value of this parameter is\n ``6.0``. Lower values of the penalty shrink the estimated\n size history towards a line. If your estimates exhibit too much\n oscillation, try decreasing the value of this parameter. (Note that this\n behavior is different than in versions 1.7.0 and earlier.)\n\n- ``--ftol``: This parameter specifies a threshold for stopping the\n EM algorithm when the relative improvement in log-likelihood becomes\n small. The default value is ``1e-4``. If the tolerance is ``epsilon``\n and ``x'``/``x`` are the new and old estimates, the algorithm will\n terminate when ``[loglik(x') - loglik(x)] / loglik(x) < epsilon``.\n Increasing values of ``epsilon`` will cause the optimizer to stop\n earlier, potentially preventing overfitting.\n\n- ``--knots``: This parameter specifies the number of spline knots \n used in the underlying representation of the size history. The default\n value is ``8``. Using fewer knots can lead to smoother fits, however\n underspecifying this parameter may smooth out interesting features of\n the size history.\n\nA useful diagnostic for understanding the final output of SMC++ are\nthe sequence of intermediate estimates ``.model.iter.json`` which\nare saved by ``--estimate`` in the ``--output`` directory. By plotting\nthese, you can get a sense of whether the optimizer is overfitting and\nrequires additional regularization.\n\nFrequently asked questions\n==========================\n1. *The binary installer dies with the error message*:: \n\n ImportError: /lib64/libc.so.6: version `GLIBC_2.14' not found (required by ...).\n \n *How can I fix this*?\n\n Users of RedHat/CentOS clusters commonly report this error. It is due \n to a ``glibc`` version mismatch between your system and\n the build server used to create the binary installers.\n We are not able to create binaries for older versions of ``glibc``. Your\n options are to either a) upgrade ``glibc`` on your system (which would\n probably require upgrading your operating system); or b) build SMC++\n yourself by following the `build instructions`_. Please note that\n linking a different version of ``glibc`` at runtime is **not** supported, \n and will likely cause random crashes.\n\n2. *SMC++ claims that my population crashed in the very recent past. What's\n going on*?\n\n Typically this is due to long runs of homozygosity (ROH) in the data, which can arise for\n one of several reasons:\n\n 1. The population legitimately experienced a recent crash, leading to inbreeding;\n 2. One or more selective sweeps occured; or\n 3. Uncalled regions in your VCF were not marked as such before running vcf2smc_. \n\n #1 represents real signal, while #2 and #3 should be filtered out using the ``-m`` \n option of vcf2smc_ and/or the ``-c`` option of estimate_.\n \n3. *How do I get the estimated recombination rate*?\n \n The ``model.final.json`` output file contains fields named ``rho`` and ``N0``. ``rho`` is \n the estimated population-scaled recombination rate per base-pair. \n To convert it to units of generations, multiply by ``2 * N0``.\n\n \nWhat to do if you encounter trouble\n===================================\nSMC++ is under active development and you may encounter difficulties in\ntrying to use it. Always make sure that you have upgraded to the `latest\nversion `_, as\nthe bug you have encountered may have already been fixed. If that does\nnot work, then:\n\n- If you believe you have encountered a **bug** in the software\n(unexpected crash, high memory usage, etc.) please `file an issue\n`_ in our bug tracker.\n- If you would like assistance in interpreting the results, please\ne-mail me directly. I will do my best to try and help, but please\nunderstand that I have limited time to respond to such inquiries.\n \nIn both cases, you will receive a faster response if you include as\nmuch detail as possible about your data set (sample size, # of contigs,\netc.), system and, where applicable, the ``.debug.txt`` log file saved\nby SMC++ in the output directory specified to the ``estimate`` command.\n\nFile formats\n============\n\nInput data format\n-----------------\nThe data files should be ASCII text and can optionally be gzipped. The\nformat of each line of the data file is as follows::\n\n [ ]\n\nExplanation of each column:\n\n- ``span`` gives the number of contiguous bases at which this\n observation occurred. Hence, it will generally be ``1`` for SNPs and\n greater than one for a stretch of nonsegregating sites.\n- Then, there are three columns ``d``/``u``/``n`` for each population:\n o ``d`` Gives the genotype (``0``, ``1``, or ``2``) of the\n distinguished individual. If the genotype of the distinguished\n individual is not known, this should be set to ``-1``.\n o The next column ``u`` is the total number of derived alleles found\n in the remainder of the (undistinguished) sample at the site(s).\n o The final column ``n`` is the *haploid* sample size (number of\n non-missing observations) in the undistinguished portion of the\n sample.\n\nFor example, consider the following set of genotypes at a set of 10\ncontiguous bases on three diploid individuals in one population::\n\n dist. ..1..N...2\n .....N...1\n 2N....+...\n\nThe distinguished individual is row one. A ``.`` indicates that the\nindividual is homozygous for the ancestral allele, while an integer\nindicates that that individual possesses ``(1,2)`` copies of the derived\nallele. An ``N`` indicates a missing genotype at that position. Finally,\nthe ``+`` in column seven indicates that individual three possessed the\nancestral allele on one chromosome, and had a missing observation on the\nother chromosome (this would be coded as ``0/.`` in a VCF).\n\nThe SMC++ format for this input file is::\n\n 1 0 2 4\n 1 0 0 2\n 1 1 0 4\n 2 0 0 4\n 1 -1 0 2\n 1 0 0 3\n 2 0 0 4\n 1 2 1 4\n\n\nThe data files also include a custom metadata header with some\nadditional information about the populations. For this reason, it is\nadvised to please use the included vcf2smc_ tool in order to translate\nfrom the ``VCF`` to ``SMC`` format.\n\nOutput data format\n------------------\nUpon completion, SMC++ will write a `JSON-formatted\n`_ model file into the into the\nanalysis directory. 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terhorst/smcpp

Tags for terhorst/smcpp