# QCA minimization using the enhanced Quine-McCluskey algorithm

## Description

This function performs the QCA minimization of an input truth table, or if the input is a dataset the minimization it minimizes a set of causal conditions with respect to an outcome. Three minimization methods are available: the classical Quine-McCluskey, the enhanced Quine-McCluskey and the latest Consistency Cubes algorithm that is built for performance.

All algorithms return the same, exact solutions, see Dusa (2017) and Dusa and Thiem (2015)

## Usage

    minimize(input, include = "", exclude = NULL, dir.exp = "",
             pi.cons = 0, pi.depth = 0, sol.cons = 0, sol.cov = 1, sol.depth = 0,
             min.pin = FALSE, row.dom = FALSE, all.sol = FALSE,
             details = FALSE, use.tilde = FALSE, method = "CCubes", ...)

## Arguments

`input` |  |  |  | A truth table object (preferred) or a data frame containing calibrated causal conditions and an outcome.
`include` |  |  |  | A vector of other output values to include in the minimization process.
`exclude` |  |  |  | A vector of row numbers from the truth table, or a matrix of causal combinations to exclude from the minimization process.
`dir.exp` |  |  |  | A vector of directional expectations to derive intermediate solutions.
`pi.cons` |  |  |  | Numerical fuzzy value between 0 and 1, minimal consistency threshold for a prime implicant to be declared as sufficient.
`pi.depth` |  |  |  | Integer, a maximum number of causal conditions to be used when searching for conjunctive prime implicants.
`sol.cons` |  |  |  | Numerical fuzzy value between 0 and 1, minimal consistency threshold for a model to be declared as sufficient.
`sol.cov` |  |  |  | Numerical fuzzy value between 0 and 1, minimal coverage threshold for a model to be declared as necessary.
`sol.depth` |  |  |  | Integer, a maximum number of prime implicants to be used when searching for disjunctive solutions.
`min.pin` |  |  |  | Logical, terminate the search at the depth where newly found prime implicants do not contribute to minimally solving the PI chart.
`row.dom` |  |  |  | Logical, perform row dominance in the prime implicants' chart to eliminate redundant prime implicants.
`all.sol` |  |  |  | Logical, search for all possible solutions even of not minimal.
`details` |  |  |  | Logical, print more details about the solution.
`use.tilde` |  |  |  | Logical, use tilde to signal the absence of conditions.
`method` |  |  |  | Minimization method, one of "CCubes" (default), or "QMC" the classical Quine-McCluskey, or "eQMC" the enhanced Quine-McCluskey.
`...` |  |  |  | Other arguments to be passed to function `truthTable()`.

## Details

Most of the times, this function takes a truth table object as the `input` for the minimization procedure, but the same argument can refer to a data frame containing calibrated columns.

For the later case, the function `minimize()` originally had some additional formal arguments which were sent to the function `truthTable()`: `outcome`, `conditions`, `n.cut`, `incl.cut`, `show.cases`, `use.letters` and `inf.test`.

All of these parameters are still possible with function `minimize()`, but since they are sent to the `truthTable()` function anyway, it is unnecessary to duplicate their explanation here. The only situation which does need an additional description relates to the argument `outcome`, where unlike `truthTable()` which accepts a single one, the function `minimize()` accepts multiple outcomes and performs a minimization for each of them (a situation when all columns are considered causal conditions).

The argument `include` specifies which other truth table rows are included in the minimization process. Most often, the remainders are included but any value accepted in the argument `explain` is also accepted in the argument `include`.

The argument `exclude` is used to exclude truth table rows from the minimization process, from the positive configurations and/or from the remainders. It can be specified as a vector of truth table line numbers, or as a matrix of causal combinations.

The argument `dir.exp` is used to specify directional expectations, as described by Ragin (2003). They can be specified using SOP expressions, which opens up the possibility to experiment with conjunctural directional expectations. Don't care conditions are simply left unspecified.

Activating the `details` argument has the effect of printing parameters of fit for each prime implicant and each overall solution, the essential prime implicants being listed in the top part of the table. It also prints the truth table, in case the argument `input` has been provided as a data frame instead of a truth table object.

The argument `use.tilde` signals the absence of a causal condition, in a sufficiency relation with the outcome, using a tilde sign `"~"`. It is ignored if the data is multivalent.

By default, the package QCA employes a different search algorithm based on Consistency Cubes (Dusa, 2017), analysing all possible combinations of causal conditions and all possible combinations of their respective levels. The structure of the input dataset (number of causal conditions, number of levels, number of unique rows in the truth table) has a direct implication on the search time, as all of those characteristics become entry parameters when calculating all possible combinations.

Consequently, two kinds of depth arguments are provided:

`pi.depth` |  |  |  | the maximum number of causal conditions needed to construct a prime implicant, the complexity level where the search can be stopped, as long as the PI chart can be solved.
`sol.depth` |  |  |  | the maximum number of prime implicants needed to find a solution (to cover all initial positive output configurations)

These arguments introduce a possible new way of deriving prime implicants and solutions, that can lead to different results (i.e. even more parsimonious) compared to the classical Quine-McCluskey. When either of them is modified from the default value of 0, the minimization method is automatically set to `"CCubes"` and the remainders are automatically included in the minimization.

The search time is larger the higher these depths, or inversely the search time can be significantly shorter if these depths are smaller. Irrespective of how large `pi.depth` is, the algorithm will always stop at a maximum complexity level where no new, non-redundant prime implicants are found.

`sol.depth` is relevant only when solving the PI chart by activating the argument `all.sol`. In such a situation, the number of combinations of all possible numbers of prime implicants is potentially too large to be solved in a polynomial time and if not otherwise specified, the depth for the disjunctive solutions is automatically bounded to 5 prime implicants.

The default method to solve the PI chart (when `all.sol = FALSE`), is to find the minimal number (`k`) of prime implicants needed to cover all initial positive output configurations, then it exhaustively searches through all possible disjunctions of `k` prime implicants which do cover those configurations.

The argument `min.pin` introduces an additional parameter to control when to stop the search for prime implicants. It is based on the observation by Dusa (2017) that out of the entire set of non redundant prime implicants, only a subset actually contribute to solving the chart with disjunctions of `k` PIs. The search depth can be shortened at the level where the next subset of PIs do not contribute to solving the PI chart, thus avoiding to spend unnecessary time on finding the maximal number of non-redundant PIs. Instead, it finds the set of minimal (`"min"`) number of PIs (`"pin"`) necessary to obtain exactly the same solutions, with a dramatically improved overall performance.

Once the PI chart is constructed using the prime implicants found in the previous stages, the argument `row.dom` can be used to further eliminate irrelevant prime implicants when solving the PI chart, applying the principle of row dominance: if a prime implicant A covers the same (intial) positive output configurations as another prime implicant B and in the same time covers other configurations which B does not cover, then B is irrelevant and eliminated.

The argument `all.sol` automatically deactivates the argument `min.pin`, because it aims to exhaustively identify all possible non-redundant disjunctions of `n` prime implicants that solve the PI chart, where `n >= k`, with an inflated number of possible solutions. Depending on the complexity of the PI chart, sometimes it may take a very long time to identify all possible non-redundant (disjunctions that are not subsets of previously found) disjunctive solutions.

The task of solving the PI chart depends on its size, with prime implicants on the rows and the positive output configurations on the columns. Since the columns are fixed, another possible way to reduce the solving time is to eliminate redundant rows, by activating the argument `row.dom`

If minimizing a dataset instead of a truth table, unless otherwise specified the argument `incl.cut` is automatically set to the minimum value between `pi.cons` and `sol.cons`, then passed to the function `truthTable()`.

The argument `sol.cons` introduces another possibility to change the method of solving the PI chart. Normally, once the solutions are found among all possible combinations of `k` prime implicants, consistencies and coverages are subsequently calculated. When `sol.cons` is lower than 1, then solutions are searched based on their consistencies, which should be at least equal to this threshold.

## Value

An object of class `"qca"` when using a single outcome, or class `"mqca"` when using multiple outcomes. These objects are lists having the following components:

`tt` |  |  |  | The truth table object.
`options` |  |  |  | Values for the various options used in the function (including defaults).
`negatives` |  |  |  | The line number(s) of the negative configuration(s).
`initials` |  |  |  | The initial positive configuration(s).
`PIchart` |  |  |  | A list containing the PI chart(s).
`primes` |  |  |  | The prime implicant(s).
`solution` |  |  |  | A list of solution(s).
`essential` |  |  |  | A list of essential PI(s).
`pims` |  |  |  | A list of PI membership scores.
`IC` |  |  |  | The matrix containing the inclusion and coverage scores for the solution(s).
`SA` |  |  |  | A list of simplifying assumptions.
`i.sol` |  |  |  | A list of components specific to intermediate solution(s), each having a PI chart, prime implicant membership scores, (non-simplifying) easy counterfactuals and difficult counterfactuals.
`call` |  |  |  | The user's command which produced all these objects and result(s).

## References

Cebotari, V.; Vink, M.P. (2013) “A Configurational Analysis of Ethnic Protest in Europe”. International Journal of Comparative Sociology vol.54, no.4, pp.298-324.

Cebotari, V.; Vink, M.P. (2015) “Replication Data for: A configurational analysis of ethnic protest in Europe”, Harvard Dataverse, V2, DOI: 10.7910/DVN/PT2IB9

Cronqvist, L.; Berg-Schlosser, D. (2009) “Multi-Value QCA (mvQCA)”, in Rihoux, B.; Ragin, C. (eds.) Configurational Comparative Methods. Qualitative Comparative Analysis (QCA) and Related Techniques, SAGE.

Dusa, A.; Thiem, A. (2015) “Enhancing the Minimization of Boolean and Multivalue Output Functions With eQMC” Journal of Mathematical Sociology vol.39, no.2, pp.92-108.

Ragin, C.C. (2003) Recent Advances in Fuzzy-Set Methods and Their Application to Policy Questions. WP 2003-9, COMPASSS.

Ragin, C.C. (2009) “Qualitative Comparative Analysis Using Fuzzy-Sets (fsQCA)”, in Rihoux, B.; Ragin, C. (eds.) Configurational Comparative Methods. Qualitative Comparative Analysis (QCA) and Related Techniques, SAGE.

Ragin, C.C.; Strand, S.I. (2008) “Using Qualitative Comparative Analysis to Study Causal Order: Comment on Caren and Panofsky (2005).” Sociological Methods & Research vol.36, no.4, pp.431-441.

Rihoux, B.; De Meur, G. (2009) “Crisp Sets Qualitative Comparative Analysis (mvQCA)”, in Rihoux, B.; Ragin, C. (eds.) Configurational Comparative Methods. Qualitative Comparative Analysis (QCA) and Related Techniques, SAGE.

## Examples

    # -----
    # Lipset binary crisp data
    data(LC)

    # the associated truth table
    ttLC <- truthTable(LC, "SURV", sort.by = "incl, n")
    ttLC

      OUT: outcome value
        n: number of cases in configuration
     incl: sufficiency inclusion score

        DEV URB LIT IND STB OUT n  incl  PRI
    32   1   1   1   1   1   1  4  1.000 1.000
    22   1   0   1   0   1   1  2  1.000 1.000
    24   1   0   1   1   1   1  2  1.000 1.000
     1   0   0   0   0   0   0  3  0.000 0.000
     2   0   0   0   0   1   0  2  0.000 0.000
     5   0   0   1   0   0   0  2  0.000 0.000
     6   0   0   1   0   1   0  1  0.000 0.000
    23   1   0   1   1   0   0  1  0.000 0.000
    31   1   1   1   1   0   0  1  0.000 0.000

    # conservative solution (Rihoux & De Meur 2009, p.57)
    cLC <- minimize(ttLC)
    cLC

    M1: DEV*urb*LIT*STB + DEV*LIT*IND*STB <=> SURV

    # view the Venn diagram for the associated truth table
    library(venn)
    venn(cLC)

    # add details and case names
    minimize(ttLC, details = TRUE, show.cases = TRUE)

    n OUT = 1/0/C: 8/10/0
      Total      : 18

    Number of multiple-covered cases: 2

    M1: DEV*urb*LIT*STB + DEV*LIT*IND*STB <=> SURV

                        inclS  PRI    covS   covU   cases
    ------------------------------------------------------------------
    1  DEV*urb*LIT*STB  1.000  1.000  0.500  0.250  FI,IE; FR,SE
    2  DEV*LIT*IND*STB  1.000  1.000  0.750  0.500  FR,SE; BE,CZ,NL,UK
    ------------------------------------------------------------------
       M1               1.000  1.000  1.000

    # negating the outcome
    ttLCn <- truthTable(LC, "~SURV", sort.by = "incl, n")
    minimize(ttLCn)

    M1: dev*urb*ind + DEV*LIT*IND*stb <=> surv

    # using a tilde instead of upper/lower case names
    minimize(ttLCn, use.tilde = TRUE)

    M1: ~DEV*~URB*~IND + DEV*LIT*IND*~STB <=> ~SURV

    # parsimonious solution, positive output
    pLC <- minimize(ttLC, include = "?", details = TRUE, show.cases = TRUE)
    pLC

    n OUT = 1/0/C: 8/10/0
      Total      : 18

    Number of multiple-covered cases: 0

    M1: DEV*STB <=> SURV

                inclS  PRI    covS   covU   cases
    -----------------------------------------------------------------
    1  DEV*STB  1.000  1.000  1.000    -    FI,IE; FR,SE; BE,CZ,NL,UK
    -----------------------------------------------------------------
       M1       1.000  1.000  1.000

    # the associated simplifying assumptions
    pLC$SA

    $M1
       DEV URB LIT IND STB
    18   1   0   0   0   1
    20   1   0   0   1   1
    26   1   1   0   0   1
    28   1   1   0   1   1
    30   1   1   1   0   1

    # parsimonious solution, negative output
    pLCn <- minimize(ttLCn, include = "?", details = TRUE, show.cases = TRUE)
    pLCn

    n OUT = 1/0/C: 10/8/0
      Total      : 18

    Number of multiple-covered cases: 5

    M1: dev + stb <=> surv

            inclS  PRI    covS   covU   cases
    --------------------------------------------------------------
    1  dev  1.000  1.000  0.800  0.300  GR,PT,ES; IT,RO; HU,PL; EE
    2  stb  1.000  1.000  0.700  0.200  GR,PT,ES; HU,PL; AU; DE
    --------------------------------------------------------------
       M1   1.000  1.000  1.000

    # -----
    # Lipset multi-value crisp data (Cronqvist & Berg-Schlosser 2009, p.80)
    data(LM)

    # truth table
    ttLM <- truthTable(LM, "SURV", conditions = "DEV, URB, LIT, IND",
            sort.by = "incl", show.cases = TRUE)

    # conservative solution, positive output
    minimize(ttLM, details = TRUE, show.cases = TRUE)

    n OUT = 1/0/C: 7/11/0
      Total      : 18

    Number of multiple-covered cases: 0

    M1: DEV{2}*LIT{1}*IND{1} + DEV{1}*URB{0}*LIT{1}*IND{0} => SURV{1}

                                    inclS  PRI    covS   covU   cases
    ---------------------------------------------------------------------------
    1  DEV{2}*LIT{1}*IND{1}         1.000  1.000  0.625  0.625  FR,SE; BE,NL,UK
    2  DEV{1}*URB{0}*LIT{1}*IND{0}  1.000  1.000  0.250  0.250  FI,IE
    ---------------------------------------------------------------------------
       M1                           1.000  1.000  0.875

    # parsimonious solution, positive output
    minimize(ttLM, include = "?", details = TRUE, show.cases = TRUE)

    n OUT = 1/0/C: 7/11/0
      Total      : 18

    Number of multiple-covered cases: 0

    M1: DEV{2} + DEV{1}*IND{0} => SURV{1}

                      inclS  PRI    covS   covU   cases
    -------------------------------------------------------------
    1  DEV{2}         1.000  1.000  0.625  0.625  FR,SE; BE,NL,UK
    2  DEV{1}*IND{0}  1.000  1.000  0.250  0.250  FI,IE
    -------------------------------------------------------------
       M1             1.000  1.000  0.875

    # negate the outcome
    ttLMn <- truthTable(LM, "~SURV", conditions = "DEV, URB, LIT, IND",
             sort.by = "incl", show.cases = TRUE)

    # conservative solution, negative output
    minimize(ttLMn, details = TRUE, show.cases = TRUE)

    n OUT = 1/0/C: 9/9/0
      Total      : 18

    Number of multiple-covered cases: 0

    M1: DEV{0}*URB{0}*IND{0} + DEV{1}*URB{0}*LIT{1}*IND{1} => ~SURV{1}

                                    inclS  PRI    covS   covU   cases
    ------------------------------------------------------------------------------------
    1  DEV{0}*URB{0}*IND{0}         1.000  1.000  0.800  0.800  GR,IT,PT,RO,ES; EE,HU,PL
    2  DEV{1}*URB{0}*LIT{1}*IND{1}  1.000  1.000  0.100  0.100  AU
    ------------------------------------------------------------------------------------
       M1                           1.000  1.000  0.900

    # parsimonious solution, positive output
    minimize(ttLMn, include = "?", details = TRUE, show.cases = TRUE)

    n OUT = 1/0/C: 9/9/0
      Total      : 18

    Number of multiple-covered cases: 0

    M1: DEV{0} + DEV{1}*URB{0}*IND{1} => ~SURV{1}

                             inclS  PRI    covS   covU   cases
    -----------------------------------------------------------------------------
    1  DEV{0}                1.000  1.000  0.800  0.800  GR,IT,PT,RO,ES; EE,HU,PL
    2  DEV{1}*URB{0}*IND{1}  1.000  1.000  0.100  0.100  AU
    -----------------------------------------------------------------------------
       M1                    1.000  1.000  0.900

    # -----
    # Lipset fuzzy sets data (Ragin 2009, p.112)
    data(LF)

    # truth table using a very low inclusion cutoff
    ttLF <- truthTable(LF, "SURV", incl.cut = 0.8,
            show.cases = TRUE, sort.by="incl")

    # conservative solution
    minimize(ttLF, details = TRUE, show.cases = TRUE)

    n OUT = 1/0/C: 6/12/0
      Total      : 18

    Number of multiple-covered cases: 0

    M1: DEV*URB*LIT*IND*STB + DEV*urb*LIT*ind*STB => SURV

                            inclS  PRI    covS   covU   cases
    ---------------------------------------------------------------
    1  DEV*URB*LIT*IND*STB  0.904  0.886  0.454  0.393  BE,CZ,NL,UK
    2  DEV*urb*LIT*ind*STB  0.804  0.719  0.265  0.204  FI,IE
    ---------------------------------------------------------------
       M1                   0.870  0.843  0.658

    # parsimonious solution
    minimize(ttLF, include = "?", details = TRUE)

    n OUT = 1/0/C: 6/12/0
      Total      : 18

    Number of multiple-covered cases: 0

    M1: DEV*ind + URB*STB => SURV

                inclS  PRI    covS   covU   cases
    ---------------------------------------------------
    1  DEV*ind  0.815  0.721  0.284  0.194  FI,IE
    2  URB*STB  0.874  0.845  0.520  0.430  BE,CZ,NL,UK
    ---------------------------------------------------
       M1       0.850  0.819  0.714

    # intermediate solution using directional expectations
    minimize(ttLF, include = "?", details = TRUE, show.cases = TRUE,
                 dir.exp = "DEV, URB, LIT, IND, STB")

    n OUT = 1/0/C: 6/12/0
      Total      : 18

    From C1P1:

    Number of multiple-covered cases: 0

    M1:    DEV*URB*LIT*STB + DEV*LIT*ind*STB => SURV

                        inclS  PRI    covS   covU   cases
    -----------------------------------------------------------
    1  DEV*URB*LIT*STB  0.901  0.879  0.468  0.378  BE,CZ,NL,UK
    2  DEV*LIT*ind*STB  0.814  0.721  0.282  0.191  FI,IE
    -----------------------------------------------------------
       M1               0.866  0.839  0.660

    # URB as a don't care condition (left unspecified) and
    # experimentally, conjunctural directional expectations
    minimize(ttLF, include = "?", details = TRUE, show.cases = TRUE,
             dir.exp = "DEV, STB, ~LIT*IND")

    n OUT = 1/0/C: 6/12/0
      Total      : 18

    From C1P1:

    Number of multiple-covered cases: 0

    M1:    DEV*URB*STB + DEV*ind*STB => SURV

                    inclS  PRI    covS   covU   cases
    -------------------------------------------------------
    1  DEV*URB*STB  0.901  0.879  0.468  0.378  BE,CZ,NL,UK
    2  DEV*ind*STB  0.814  0.721  0.282  0.191  FI,IE
    -------------------------------------------------------
       M1           0.866  0.839  0.660

    # -----
    # Cebotari & Vink (2013, 2015)
    data(CVF)

    ttCVF <- truthTable(CVF, outcome = "PROTEST", incl.cut = 0.8,
    	                show.cases = TRUE, sort.by = "incl, n")

    pCVF <- minimize(ttCVF, include = "?", details = TRUE, show.cases = TRUE)
    pCVF

    n OUT = 1/0/C: 13/16/0
      Total      : 29

    Number of multiple-covered cases: 5

    M1: natpride + DEMOC*GEOCON*POLDIS + (democ*ETHFRACT*POLDIS + DEMOC*ETHFRACT*GEOCON) => PROTEST
    M2: natpride + DEMOC*GEOCON*POLDIS + (democ*ETHFRACT*POLDIS + DEMOC*ETHFRACT*poldis) => PROTEST
    M3: natpride + DEMOC*GEOCON*POLDIS + (DEMOC*ETHFRACT*GEOCON + ETHFRACT*GEOCON*POLDIS) => PROTEST
    M4: natpride + DEMOC*GEOCON*POLDIS + (DEMOC*ETHFRACT*poldis + ETHFRACT*GEOCON*POLDIS) => PROTEST

                                                    ---------------------------------
                               inclS  PRI    covS   covU   (M1)   (M2)   (M3)   (M4)
    ---------------------------------------------------------------------------------
    1 natpride                0.899  0.807  0.597  0.121  0.132  0.122  0.136  0.126
    2 DEMOC*GEOCON*POLDIS     0.906  0.805  0.342  0.065  0.065  0.070  0.065  0.065
    ---------------------------------------------------------------------------------
    3  democ*ETHFRACT*POLDIS   0.842  0.718  0.299  0.000  0.040  0.040
    4  DEMOC*ETHFRACT*GEOCON   0.935  0.826  0.480  0.000  0.085         0.085
    5  DEMOC*ETHFRACT*poldis   0.932  0.773  0.417  0.000         0.085         0.085
    6  ETHFRACT*GEOCON*POLDIS  0.869  0.786  0.365  0.005                0.045  0.045
    ---------------------------------------------------------------------------------
       M1                      0.877  0.777  0.805
       M2                      0.877  0.777  0.805
       M3                      0.879  0.782  0.810
       M4                      0.879  0.782  0.810

                               cases
    --------------------------------
    1  natpride                CrimRussiansUkr,RussiansUkraine; HungariansYugo,KosovoAlbanians;
                               RussiansLatvia; BasquesSpain; AlbaniansFYROM
    2  DEMOC*GEOCON*POLDIS     HungariansRom,CatholicsNIreland; AlbaniansFYROM; RussiansEstonia
    --------------------------------
    3  democ*ETHFRACT*POLDIS   HungariansYugo,KosovoAlbanians; GagauzMoldova
    4  DEMOC*ETHFRACT*GEOCON   BasquesSpain; SerbsFYROM,CatalansSpain; AlbaniansFYROM; RussiansEstonia
    5  DEMOC*ETHFRACT*poldis   BasquesSpain; SerbsFYROM,CatalansSpain
    6  ETHFRACT*GEOCON*POLDIS  HungariansYugo,KosovoAlbanians; GagauzMoldova; AlbaniansFYROM;
                               RussiansEstonia
    --------------------------------

    # inspect the PI chart
    pCVF$PIchart

                            5  15 16 24 27 29 30 31 32
    natpride                x  x  -  -  x  x  -  x  -
    democ*ETHFRACT*POLDIS   -  x  x  -  -  -  -  -  -
    DEMOC*ETHFRACT*GEOCON   -  -  -  -  -  x  x  x  x
    DEMOC*ETHFRACT*poldis   -  -  -  -  -  x  x  -  -
    DEMOC*GEOCON*POLDIS     -  -  -  x  -  -  -  x  x
    ETHFRACT*GEOCON*POLDIS  -  x  x  -  -  -  -  x  x

    # DEMOC*ETHFRACT*poldis is dominated by DEMOC*ETHFRACT*GEOCON
    # using row dominance to solve the PI chart
    pCVFrd <- minimize(ttCVF, include = "?", row.dom = TRUE,
                    details = TRUE, show.cases = TRUE)

    # plot the prime implicants on the outcome
    pims <- pCVFrd$pims

    par(mfrow = c(2, 2))
    for(i in 1:4) {
        XYplot(pims[, i], CVF$PROTEST, cex.axis = 0.6)
    }

    # -----
    # temporal QCA (Ragin & Strand 2008)
    data(RS)
    minimize(RS, outcome = "REC", details = TRUE, show.cases = TRUE)

      OUT: outcome value
        n: number of cases in configuration
     incl: sufficiency inclusion score

        P  E  A  S  EBA OUT n  incl  PRI   cases
     3  0  0  0  0   -   0  3  0.000 0.000 15,16,17
    15  0  1  0  0   -   0  1  0.000 0.000 14
    22  0  1  1  1   0   1  1  1.000 1.000 13
    27  1  0  0  0   -   0  1  0.000 0.000 12
    30  1  0  0  1   -   0  3  0.000 0.000 9,10,11
    36  1  0  1  1   -   0  2  0.000 0.000 7,8
    42  1  1  0  1   -   1  1  1.000 1.000 6
    44  1  1  1  0   1   1  2  1.000 1.000 4,5
    46  1  1  1  1   0   1  1  1.000 1.000 3
    47  1  1  1  1   1   1  2  1.000 1.000 1,2

    n OUT = 1/0/C: 7/10/0
      Total      : 17

    Number of multiple-covered cases: 3

    M1: P*E*S + P*E*A*EBA + E*A*eba*S <=> REC

                  inclS  PRI    covS   covU   cases
    ---------------------------------------------------
    1  P*E*S      1.000  1.000  0.571  0.143  6; 3; 1,2
    2  P*E*A*EBA  1.000  1.000  0.571  0.286  4,5; 1,2
    3  E*A*eba*S  1.000  1.000  0.286  0.143  13; 3
    ---------------------------------------------------
       M1         1.000  1.000  1.000

## Author

Adrian Dusa

## See also

`truthTable`, `factorize`